Split (1)

train · 119k rows

conversations listlengths 3 3
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Yolo, California, USA, along the US50-E freeway, lane 4, direction of eastbound. - Today's weather: Sunny. Temperature is 9.5\\u00b0C, and visibility reaches 7.4 miles. - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 215, 176, 155, 126, 97, 70, 58, 38, 53, 50, 40 and 34, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [28, 42, 46, 58, 90, 125, 170, 198, 213, 224, 206, 185]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Sacramento, California, USA, along the US50-E freeway, lane 4, direction of eastbound. - Today's weather: Sunny. Temperature is 9.3\\u00b0C, and visibility reaches 8.1 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 240, 232, 208, 167, 168, 152, 98, 110, 101, 69, 45 and 30, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [48, 60, 89, 110, 140, 177, 228, 265, 270, 270, 272, 244]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Sacramento, California, USA, along the US50-E freeway, lane 3, direction of eastbound. - Today's weather: Sunny. Temperature is 9.3\\u00b0C, and visibility reaches 8.1 miles. - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 170, 152, 122, 100, 83, 75, 50, 48, 44, 27, 19 and 15, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [26, 34, 46, 57, 88, 116, 142, 158, 176, 177, 178, 160]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Yolo, California, USA, along the US50-W freeway, lane 3, direction of westbound. - Today's weather: Sunny. Temperature is 9.5\\u00b0C, and visibility reaches 7.4 miles. - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 177, 176, 142, 114, 110, 79, 48, 62, 62, 40, 27 and 38, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [36, 47, 50, 84, 139, 212, 246, 274, 284, 248, 226, 216]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in El Dorado, California, USA, along the US50-W freeway, lane 3, direction of westbound. - Today's weather: Sunny. Temperature is 9.3\\u00b0C, and visibility reaches 8.1 miles. - Region information: including transportation areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 122, 104, 74, 62, 52, 51, 42, 54, 50, 41, 29 and 30, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [30, 33, 40, 62, 98, 128, 152, 154, 159, 150, 153, 152]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Sacramento, California, USA, along the SR51-N freeway, lane 3, direction of northbound. - Today's weather: Sunny. Temperature is 9.5\\u00b0C, and visibility reaches 7.4 miles. - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 358, 330, 320, 259, 248, 216, 156, 181, 164, 124, 73 and 62, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [59, 78, 104, 129, 224, 340, 411, 461, 502, 539, 535, 496]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Sacramento, California, USA, along the SR51-N freeway, lane 3, direction of northbound. - Today's weather: Sunny. Temperature is 9.3\\u00b0C, and visibility reaches 8.1 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 236, 244, 232, 192, 193, 175, 120, 134, 141, 96, 50 and 34, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [34, 55, 73, 85, 128, 168, 193, 241, 250, 262, 256, 238]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Sacramento, California, USA, along the SR51-S freeway, lane 3, direction of southbound. - Today's weather: Sunny. Temperature is 9.3\\u00b0C, and visibility reaches 8.1 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 312, 296, 260, 235, 198, 166, 134, 135, 146, 118, 85 and 68, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [74, 94, 94, 110, 167, 220, 264, 299, 347, 331, 311, 309]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Placer, California, USA, along the I80-W freeway, lane 3, direction of westbound. - Today's weather: Sunny. Temperature is 9.3\\u00b0C, and visibility reaches 8.1 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 242, 218, 176, 147, 135, 126, 107, 113, 112, 97, 92 and 86, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [93, 106, 114, 144, 193, 242, 282, 312, 322, 305, 316, 286]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Sacramento, California, USA, along the SR99-N freeway, lane 4, direction of northbound. - Today's weather: Sunny. Temperature is 9.5\\u00b0C, and visibility reaches 7.4 miles. - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 298, 275, 242, 222, 196, 167, 113, 123, 125, 83, 60 and 50, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [60, 84, 88, 116, 167, 214, 255, 289, 310, 329, 316, 304]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Sacramento, California, USA, along the SR99-S freeway, lane 2, direction of southbound. - Today's weather: Sunny. Temperature is 9.5\\u00b0C, and visibility reaches 7.4 miles. - Region information: including transportation areas, residential areas and educational areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 116, 101, 82, 62, 62, 57, 36, 52, 52, 35, 24 and 17, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [18, 22, 31, 44, 62, 92, 114, 119, 116, 115, 119, 121]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Sacramento, California, USA, along the SR99-S freeway, lane 2, direction of southbound. - Today's weather: Sunny. Temperature is 9.1\\u00b0C, and visibility reaches 9.3 miles. - Region information: including transportation areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 81, 67, 55, 46, 33, 22, 16, 17, 25, 16, 14 and 20, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [18, 22, 28, 34, 54, 68, 90, 89, 100, 95, 89, 84]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Contra Costa, California, USA, along the SR24-W freeway, lane 4, direction of westbound. - Today's weather: Sunny. Temperature is 9.6\\u00b0C, and visibility reaches 7.4 miles. - Region information: including transportation areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 357, 340, 269, 228, 204, 179, 105, 77, 114, 29, 1 and 3, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [31, 80, 22, 108, 164, 228, 295, 339, 362, 370, 329, 313]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Solano, California, USA, along the I80-W freeway, lane 3, direction of westbound. - Today's weather: Sunny. Temperature is 9.5\\u00b0C, and visibility reaches 7.4 miles. - Region information: including transportation areas, educational areas and residential areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 262, 256, 213, 190, 162, 121, 74, 42, 25, 20, 18 and 28, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [79, 156, 244, 256, 228, 229, 250, 276, 293, 318, 294, 274]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Santa Clara, California, USA, along the US101-N freeway, lane 4, direction of northbound. - Today's weather: Sunny. Temperature is 11.5\\u00b0C, and visibility reaches 5.0 miles. - Region information: including transportation areas, residential areas and educational areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 240, 238, 163, 122, 94, 72, 54, 64, 74, 42, 25 and 20, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [28, 32, 41, 60, 91, 136, 190, 224, 264, 246, 252, 281]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in San Mateo, California, USA, along the US101-N freeway, lane 5, direction of northbound. - Today's weather: Sunny. Temperature is 11.2\\u00b0C, and visibility reaches 7.6 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 470, 498, 464, 426, 411, 339, 220, 147, 177, 124, 106 and 96, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [105, 146, 151, 196, 253, 330, 402, 464, 451, 480, 440, 419]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Marin, California, USA, along the US101-N freeway, lane 4, direction of northbound. - Today's weather: Sunny. Temperature is 9.6\\u00b0C, and visibility reaches 7.4 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 278, 235, 208, 177, 176, 169, 144, 221, 259, 184, 126 and 89, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [85, 92, 121, 158, 197, 254, 298, 334, 338, 319, 322, 302]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Santa Clara, California, USA, along the US101-S freeway, lane 4, direction of southbound. - Today's weather: Sunny. Temperature is 11.5\\u00b0C, and visibility reaches 5.0 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 201, 193, 164, 148, 130, 97, 62, 35, 18, 15, 14 and 25, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [66, 118, 176, 180, 176, 175, 185, 196, 197, 218, 212, 209]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Marin, California, USA, along the US101-S freeway, lane 4, direction of southbound. - Today's weather: Sunny. Temperature is 9.6\\u00b0C, and visibility reaches 7.4 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 280, 247, 210, 196, 172, 154, 137, 113, 120, 106, 93 and 84, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [92, 119, 125, 137, 174, 228, 281, 309, 325, 321, 322, 319]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in San Mateo, California, USA, along the I280-N freeway, lane 4, direction of northbound. - Today's weather: Sunny. Temperature is 11.2\\u00b0C, and visibility reaches 7.6 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 368, 356, 310, 269, 230, 208, 145, 120, 134, 82, 56 and 44, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [49, 68, 92, 117, 173, 248, 321, 367, 379, 396, 373, 347]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Alameda, California, USA, along the I580-W freeway, lane 4, direction of westbound. - Today's weather: Sunny. Temperature is 9.6\\u00b0C, and visibility reaches 7.4 miles. - Region information: including transportation areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 349, 342, 285, 253, 216, 160, 99, 56, 34, 28, 24 and 38, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [106, 208, 324, 342, 303, 306, 333, 367, 390, 424, 393, 365]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Alameda, California, USA, along the I680-N freeway, lane 3, direction of northbound. - Today's weather: Sunny. Temperature is 11.5\\u00b0C, and visibility reaches 5.0 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 202, 196, 167, 146, 126, 106, 90, 124, 132, 92, 71 and 66, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [64, 78, 88, 112, 144, 171, 205, 217, 221, 212, 203, 200]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Contra Costa, California, USA, along the I680-N freeway, lane 5, direction of northbound. - Today's weather: Sunny. Temperature is 9.6\\u00b0C, and visibility reaches 7.4 miles. - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 526, 505, 448, 390, 336, 300, 292, 276, 353, 277, 221 and 147, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [118, 153, 193, 223, 288, 384, 481, 530, 552, 558, 526, 509]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Santa Clara, California, USA, along the I680-S freeway, lane 4, direction of southbound. - Today's weather: Sunny. Temperature is 11.5\\u00b0C, and visibility reaches 5.0 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 314, 292, 254, 205, 168, 134, 63, 94, 131, 77, 10 and 1, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [1, 1, 6, 33, 125, 201, 266, 310, 340, 344, 341, 317]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Contra Costa, California, USA, along the I680-S freeway, lane 6, direction of southbound. - Today's weather: Sunny. Temperature is 9.6\\u00b0C, and visibility reaches 7.4 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 287, 263, 224, 195, 156, 134, 100, 124, 134, 101, 82 and 64, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [63, 73, 90, 117, 154, 204, 248, 272, 306, 302, 298, 280]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Santa Clara, California, USA, along the I880-N freeway, lane 6, direction of northbound. - Today's weather: Sunny. Temperature is 11.5\\u00b0C, and visibility reaches 5.0 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 464, 460, 383, 309, 247, 212, 154, 204, 221, 142, 89 and 68, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [74, 80, 95, 128, 193, 278, 365, 420, 440, 436, 424, 440]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Alameda, California, USA, along the I880-S freeway, lane 4, direction of southbound. - Today's weather: Sunny. Temperature is 10.4\\u00b0C, and visibility reaches 5.2 miles. - Region information: including transportation areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 352, 336, 283, 221, 194, 173, 122, 150, 213, 144, 90 and 59, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [52, 68, 76, 107, 167, 241, 320, 365, 375, 361, 345, 353]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 5 in Santa Cruz, California, USA, along the SR17-N freeway, lane 3, direction of northbound. - Today's weather: Sunny. Temperature is 11.5\\u00b0C, and visibility reaches 5.0 miles. - Region information: including commercial areas, transportation areas and residential areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 209, 157, 105, 80, 67, 49, 36, 55, 39, 23, 10 and 1, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [0, 0, 0, 8, 94, 157, 198, 212, 221, 235, 265, 250]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 6 in Kern, California, USA, along the SR99-S freeway, lane 3, direction of southbound. - Today's weather: Sunny. Temperature is 10.7\\u00b0C, and visibility reaches 3.1 miles. - Region information: including transportation areas, residential areas and educational areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 153, 129, 107, 77, 59, 44, 32, 36, 37, 26, 23 and 18, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [37, 31, 47, 68, 90, 118, 145, 178, 201, 216, 241, 247]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the SR2-W freeway, lane 4, direction of westbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 6.6 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 281, 228, 200, 194, 181, 147, 87, 98, 104, 70, 54 and 36, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [43, 66, 72, 90, 133, 196, 271, 264, 236, 226, 230, 260]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the SR2-W freeway, lane 4, direction of westbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 6.6 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 136, 119, 115, 100, 100, 76, 42, 52, 57, 36, 27 and 18, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [23, 34, 36, 45, 70, 98, 128, 136, 124, 125, 131, 124]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I5-S freeway, lane 5, direction of southbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 6.6 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 501, 464, 412, 372, 322, 253, 163, 98, 64, 55, 62 and 132, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [378, 552, 572, 542, 513, 506, 508, 521, 530, 560, 575, 572]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I10-E freeway, lane 4, direction of eastbound. - Today's weather: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.4 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 400, 369, 323, 288, 245, 191, 124, 74, 46, 40, 44 and 91, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [262, 414, 482, 456, 399, 390, 397, 412, 423, 457, 476, 481]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I10-W freeway, lane 4, direction of westbound. - Today's weather: Sunny. Temperature is 13.8\\u00b0C, and visibility reaches 9.3 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 341, 314, 332, 320, 310, 281, 249, 276, 302, 280, 264 and 242, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [238, 238, 249, 262, 292, 330, 369, 393, 399, 408, 394, 393]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the SR47-S freeway, lane 2, direction of southbound. - Today's weather: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.4 miles. - Region information: including residential areas, commercial areas and transportation areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 86, 74, 67, 61, 59, 54, 50, 54, 53, 49, 40 and 38, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [43, 49, 47, 48, 55, 61, 70, 77, 84, 85, 91, 88]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the SR60-E freeway, lane 4, direction of eastbound. - Today's weather: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.4 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 485, 454, 396, 302, 249, 210, 151, 233, 313, 205, 118 and 76, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [66, 79, 102, 132, 190, 258, 336, 423, 428, 424, 400, 390]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the SR60-E freeway, lane 5, direction of eastbound. - Today's weather: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.4 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 511, 497, 428, 366, 326, 282, 232, 274, 350, 265, 188 and 146, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [140, 146, 168, 204, 259, 312, 385, 433, 459, 442, 424, 423]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the SR60-E freeway, lane 4, direction of eastbound. - Today's weather: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.4 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 479, 459, 408, 338, 294, 239, 180, 218, 304, 208, 132 and 93, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [85, 94, 113, 156, 209, 274, 341, 385, 425, 430, 409, 382]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the SR60-E freeway, lane 4, direction of eastbound. - Today's weather: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.0 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 423, 441, 375, 292, 223, 182, 134, 168, 299, 188, 103 and 58, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [56, 54, 74, 118, 162, 214, 287, 342, 348, 358, 341, 320]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the SR91-W freeway, lane 4, direction of westbound. - Today's weather: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.4 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 400, 369, 323, 288, 245, 191, 124, 74, 46, 40, 44 and 91, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [262, 414, 482, 456, 399, 390, 397, 412, 423, 457, 476, 481]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the US101-N freeway, lane 4, direction of northbound. - Today's weather: Sunny. Temperature is 13.7\\u00b0C, and visibility reaches 6.3 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 352, 307, 256, 208, 161, 140, 106, 130, 174, 94, 72 and 41, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [43, 62, 88, 118, 175, 263, 356, 403, 417, 420, 396, 352]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the US101-S freeway, lane 4, direction of southbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 6.6 miles. - Region information: including residential areas, commercial areas and transportation areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 364, 348, 350, 359, 353, 337, 262, 324, 336, 304, 230 and 159, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [111, 108, 111, 139, 192, 256, 347, 438, 406, 386, 396, 376]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I110-N freeway, lane 4, direction of northbound. - Today's weather: Sunny. Temperature is 13.8\\u00b0C, and visibility reaches 3.2 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 432, 464, 474, 454, 483, 365, 292, 367, 411, 314, 230 and 176, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [210, 221, 231, 253, 304, 365, 417, 469, 492, 513, 467, 448]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the SR134-E freeway, lane 4, direction of eastbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 6.6 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 392, 399, 353, 306, 266, 212, 126, 191, 298, 211, 114 and 82, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [116, 147, 144, 150, 193, 270, 319, 340, 349, 349, 349, 317]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the SR134-E freeway, lane 4, direction of eastbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 6.6 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 340, 326, 282, 210, 175, 148, 98, 128, 189, 145, 91 and 86, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [161, 192, 188, 185, 189, 237, 258, 260, 277, 288, 260, 270]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I210-E freeway, lane 5, direction of eastbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 6.6 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 400, 370, 324, 288, 245, 191, 124, 74, 47, 40, 44 and 91, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [263, 414, 482, 456, 399, 390, 397, 412, 423, 458, 476, 481]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 5, direction of northbound. - Today's weather: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.4 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 560, 516, 466, 419, 332, 274, 187, 201, 248, 201, 148 and 133, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [166, 166, 186, 223, 300, 397, 504, 612, 570, 574, 563, 538]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 5, direction of northbound. - Today's weather: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.4 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 553, 503, 464, 422, 350, 307, 226, 236, 269, 232, 185 and 171, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [198, 194, 212, 234, 304, 386, 476, 554, 540, 543, 527, 514]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 4, direction of northbound. - Today's weather: Sunny. Temperature is 13.8\\u00b0C, and visibility reaches 3.2 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 316, 281, 273, 230, 223, 200, 141, 190, 220, 116, 74 and 65, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [78, 100, 120, 139, 189, 256, 310, 357, 315, 368, 329, 300]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 5, direction of northbound. - Today's weather: Sunny. Temperature is 13.7\\u00b0C, and visibility reaches 6.3 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 619, 538, 538, 463, 420, 400, 332, 352, 468, 279, 199 and 184, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [188, 199, 245, 277, 340, 426, 493, 544, 558, 577, 550, 526]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 6, direction of northbound. - Today's weather: Sunny. Temperature is 13.7\\u00b0C, and visibility reaches 6.3 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 712, 610, 586, 494, 434, 424, 340, 362, 512, 164, 205 and 133, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [133, 141, 200, 246, 313, 432, 530, 597, 613, 651, 600, 580]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 4, direction of northbound. - Today's weather: Sunny. Temperature is 13.7\\u00b0C, and visibility reaches 6.3 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 407, 386, 356, 302, 252, 235, 176, 206, 280, 150, 136 and 77, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [76, 92, 132, 158, 198, 254, 316, 348, 378, 386, 371, 367]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I405-S freeway, lane 4, direction of southbound. - Today's weather: Sunny. Temperature is 13.8\\u00b0C, and visibility reaches 3.2 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 498, 456, 408, 339, 289, 243, 186, 215, 234, 166, 109 and 82, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [96, 118, 146, 183, 242, 342, 425, 457, 474, 481, 427, 440]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I405-S freeway, lane 5, direction of southbound. - Today's weather: Sunny. Temperature is 13.8\\u00b0C, and visibility reaches 3.2 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 670, 570, 542, 480, 456, 412, 300, 392, 488, 275, 166 and 132, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [153, 183, 222, 254, 334, 448, 526, 608, 608, 653, 558, 564]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I605-N freeway, lane 4, direction of northbound. - Today's weather: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.4 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 634, 579, 532, 457, 416, 325, 210, 127, 83, 64, 66 and 133, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [357, 618, 694, 654, 628, 606, 648, 639, 582, 590, 614, 621]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I605-N freeway, lane 4, direction of northbound. - Today's weather: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.4 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 490, 453, 421, 349, 307, 258, 186, 231, 342, 223, 136 and 106, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [133, 167, 171, 199, 232, 308, 384, 439, 442, 425, 411, 387]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I605-N freeway, lane 4, direction of northbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 6.6 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 354, 338, 290, 231, 202, 166, 123, 147, 226, 143, 94 and 79, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [126, 168, 187, 188, 211, 263, 333, 360, 366, 342, 303, 276]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I710-S freeway, lane 4, direction of southbound. - Today's weather: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.4 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 273, 278, 266, 263, 227, 207, 170, 154, 189, 147, 119 and 94, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [103, 107, 104, 113, 142, 166, 227, 278, 263, 275, 282, 314]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I710-S freeway, lane 6, direction of southbound. - Today's weather: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.4 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 400, 369, 323, 288, 245, 191, 124, 74, 47, 40, 44 and 91, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [262, 414, 481, 456, 399, 390, 397, 412, 423, 458, 476, 481]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I710-S freeway, lane 4, direction of southbound. - Today's weather: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.4 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 400, 369, 323, 288, 245, 191, 124, 74, 46, 40, 44 and 91, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [262, 414, 482, 456, 399, 390, 397, 412, 423, 457, 476, 481]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I710-S freeway, lane 5, direction of southbound. - Today's weather: Sunny. Temperature is 13.8\\u00b0C, and visibility reaches 3.2 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 248, 225, 196, 174, 148, 114, 74, 44, 28, 24, 28 and 57, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [168, 277, 325, 302, 254, 244, 246, 253, 258, 286, 322, 337]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the I10-W freeway, lane 4, direction of westbound. - Today's weather: Sunny. Temperature is 13.8\\u00b0C, and visibility reaches 9.3 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 455, 430, 359, 328, 286, 231, 166, 123, 95, 81, 122 and 344, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [416, 418, 423, 422, 430, 434, 448, 459, 470, 501, 486, 467]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the I10-W freeway, lane 4, direction of westbound. - Today's weather: Sunny. Temperature is 13.8\\u00b0C, and visibility reaches 9.3 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 486, 445, 394, 360, 306, 240, 159, 114, 84, 73, 140 and 355, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [460, 519, 509, 494, 486, 486, 500, 511, 526, 557, 567, 545]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the I10-W freeway, lane 4, direction of westbound. - Today's weather: Sunny. Temperature is 13.8\\u00b0C, and visibility reaches 9.3 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 391, 358, 292, 247, 214, 172, 122, 96, 77, 72, 121 and 267, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [347, 459, 462, 404, 386, 392, 411, 423, 448, 471, 472, 466]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the I10-W freeway, lane 4, direction of westbound. - Today's weather: Sunny. Temperature is 13.8\\u00b0C, and visibility reaches 9.3 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 462, 446, 419, 398, 344, 260, 169, 101, 69, 60, 79 and 165, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [344, 433, 476, 500, 468, 469, 478, 491, 506, 551, 591, 570]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in Riverside, California, USA, along the I10-W freeway, lane 4, direction of westbound. - Today's weather: Sunny. Temperature is 10.4\\u00b0C, and visibility reaches 9.8 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 462, 446, 419, 398, 344, 260, 169, 101, 69, 60, 79 and 165, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [344, 433, 476, 500, 468, 469, 478, 491, 506, 551, 591, 570]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in Riverside, California, USA, along the I15-N freeway, lane 4, direction of northbound. - Today's weather: Sunny. Temperature is 10.4\\u00b0C, and visibility reaches 9.8 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 462, 446, 419, 398, 344, 260, 169, 101, 69, 60, 79 and 165, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [344, 433, 476, 500, 468, 469, 478, 491, 506, 551, 591, 570]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the I15-N freeway, lane 4, direction of northbound. - Today's weather: Sunny. Temperature is 13.8\\u00b0C, and visibility reaches 9.3 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 427, 426, 378, 347, 305, 244, 162, 106, 73, 60, 59 and 94, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [160, 232, 251, 244, 266, 300, 332, 389, 435, 488, 535, 551]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the I15-N freeway, lane 4, direction of northbound. - Today's weather: Sunny. Temperature is 13.8\\u00b0C, and visibility reaches 9.3 miles. - Region information: including transportation areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 313, 303, 276, 256, 215, 173, 118, 77, 56, 45, 47 and 69, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [126, 167, 186, 191, 206, 236, 266, 286, 327, 350, 387, 414]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in Riverside, California, USA, along the SR60-E freeway, lane 2, direction of eastbound. - Today's weather: Sunny. Temperature is 10.4\\u00b0C, and visibility reaches 9.8 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 215, 230, 210, 197, 177, 143, 104, 70, 46, 40, 37 and 58, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [99, 162, 178, 190, 198, 218, 237, 251, 253, 255, 248, 236]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the SR71-S freeway, lane 3, direction of southbound. - Today's weather: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.0 miles. - Region information: including transportation areas, residential areas and educational areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 253, 231, 205, 176, 157, 124, 81, 49, 32, 26, 31 and 71, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [127, 160, 208, 237, 212, 213, 230, 246, 270, 279, 290, 329]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in Riverside, California, USA, along the SR91-W freeway, lane 3, direction of westbound. - Today's weather: Sunny. Temperature is 10.4\\u00b0C, and visibility reaches 9.8 miles. - Region information: including transportation areas, residential areas and educational areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 313, 321, 294, 258, 227, 180, 122, 84, 63, 56, 105 and 222, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [213, 284, 381, 371, 342, 357, 360, 363, 378, 395, 388, 352]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the I210-W freeway, lane 4, direction of westbound. - Today's weather: Sunny. Temperature is 13.8\\u00b0C, and visibility reaches 9.3 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 364, 331, 272, 242, 215, 168, 117, 101, 78, 69, 116 and 311, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [282, 344, 363, 318, 315, 298, 320, 320, 341, 360, 361, 367]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the I215-N freeway, lane 3, direction of northbound. - Today's weather: Sunny. Temperature is 13.8\\u00b0C, and visibility reaches 9.3 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 281, 258, 236, 215, 196, 150, 98, 62, 41, 36, 46 and 88, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [164, 288, 345, 306, 249, 255, 276, 286, 303, 335, 347, 356]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the I215-N freeway, lane 5, direction of northbound. - Today's weather: Sunny. Temperature is 13.8\\u00b0C, and visibility reaches 9.3 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 392, 376, 326, 287, 256, 212, 163, 116, 82, 65, 71 and 115, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [181, 263, 338, 348, 320, 338, 366, 399, 424, 492, 556, 578]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in Riverside, California, USA, along the I215-S freeway, lane 3, direction of southbound. - Today's weather: Sunny. Temperature is 13.8\\u00b0C, and visibility reaches 9.3 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 346, 335, 314, 299, 259, 195, 127, 76, 52, 45, 59 and 124, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [258, 325, 357, 375, 351, 352, 359, 368, 380, 413, 443, 428]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the I215-S freeway, lane 2, direction of southbound. - Today's weather: Sunny. Temperature is 13.8\\u00b0C, and visibility reaches 9.3 miles. - Region information: including transportation areas, educational areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 231, 223, 210, 199, 172, 130, 84, 51, 34, 30, 40 and 82, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [172, 216, 238, 250, 234, 234, 239, 246, 254, 276, 296, 286]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 10 in Merced, California, USA, along the I5-N freeway, lane 2, direction of northbound. - Today's weather: Sunny. Temperature is 10.5\\u00b0C, and visibility reaches 3.6 miles. - Region information: including transportation areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 143, 104, 84, 60, 53, 38, 24, 14, 9, 6, 8 and 9, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [9, 11, 24, 44, 66, 91, 121, 154, 177, 211, 215, 227]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 10 in San Joaquin, California, USA, along the I5-N freeway, lane 3, direction of northbound. - Today's weather: Fog. Temperature is 9.0\\u00b0C, and visibility reaches 6.4 miles. - Region information: including transportation areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 96, 91, 66, 59, 52, 46, 38, 37, 43, 32, 26 and 26, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [29, 34, 33, 41, 50, 66, 76, 92, 98, 106, 107, 108]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 10 in Merced, California, USA, along the SR99-N freeway, lane 2, direction of northbound. - Today's weather: Sunny. Temperature is 10.5\\u00b0C, and visibility reaches 3.6 miles. - Region information: including transportation areas, residential areas and educational areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 140, 122, 98, 70, 58, 44, 36, 34, 36, 22, 16 and 14, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [20, 27, 37, 52, 70, 97, 136, 143, 167, 185, 189, 184]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 10 in Stanislaus, California, USA, along the SR99-N freeway, lane 3, direction of northbound. - Today's weather: Fog. Temperature is 10.4\\u00b0C, and visibility reaches 5.6 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 300, 253, 216, 153, 112, 80, 58, 82, 84, 60, 41 and 42, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [55, 51, 73, 102, 136, 179, 230, 286, 308, 318, 306, 312]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 11 in San Diego, California, USA, along the I5-S freeway, lane 4, direction of southbound. - Today's weather: Fog. Temperature is 13.8\\u00b0C, and visibility reaches 4.5 miles. - Region information: including transportation areas, residential areas and educational areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 308, 310, 240, 191, 141, 107, 74, 46, 94, 90, 70 and 61, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [60, 70, 64, 100, 164, 236, 326, 370, 380, 402, 374, 349]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 11 in San Diego, California, USA, along the I8-E freeway, lane 2, direction of eastbound. - Today's weather: Fog. Temperature is 13.8\\u00b0C, and visibility reaches 4.5 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 154, 96, 81, 61, 53, 48, 34, 47, 38, 34, 32 and 32, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [24, 27, 27, 38, 55, 79, 99, 116, 121, 126, 134, 133]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 11 in San Diego, California, USA, along the I8-E freeway, lane 4, direction of eastbound. - Today's weather: Fog. Temperature is 13.8\\u00b0C, and visibility reaches 4.5 miles. - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 311, 278, 240, 190, 165, 144, 116, 124, 112, 90, 59 and 43, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [34, 50, 68, 87, 131, 170, 214, 250, 291, 307, 318, 319]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 11 in San Diego, California, USA, along the I8-E freeway, lane 2, direction of eastbound. - Today's weather: Fog. Temperature is 13.8\\u00b0C, and visibility reaches 4.5 miles. - Region information: including transportation areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 198, 155, 120, 97, 80, 66, 57, 80, 80, 51, 34 and 24, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [33, 38, 51, 78, 113, 154, 203, 243, 256, 266, 263, 272]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 11 in San Diego, California, USA, along the I15-N freeway, lane 5, direction of northbound. - Today's weather: Fog. Temperature is 13.8\\u00b0C, and visibility reaches 4.5 miles. - Region information: including transportation areas, educational areas and residential areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 327, 284, 225, 181, 156, 124, 88, 124, 132, 93, 60 and 48, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [61, 81, 108, 147, 189, 234, 291, 305, 330, 322, 330, 309]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 11 in San Diego, California, USA, along the I15-S freeway, lane 6, direction of southbound. - Today's weather: Fog. Temperature is 13.8\\u00b0C, and visibility reaches 4.5 miles. - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 386, 357, 308, 261, 224, 182, 121, 123, 142, 94, 64 and 48, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [66, 71, 94, 118, 169, 222, 292, 327, 374, 368, 366, 348]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 11 in San Diego, California, USA, along the SR52-E freeway, lane 2, direction of eastbound. - Today's weather: Fog. Temperature is 13.8\\u00b0C, and visibility reaches 4.5 miles. - Region information: including transportation areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 131, 115, 94, 72, 67, 58, 44, 44, 42, 27, 18 and 15, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [14, 20, 36, 41, 61, 87, 110, 126, 132, 136, 144, 135]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 11 in San Diego, California, USA, along the I805-N freeway, lane 5, direction of northbound. - Today's weather: Fog. Temperature is 13.8\\u00b0C, and visibility reaches 4.5 miles. - Region information: including transportation areas, educational areas and residential areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 264, 233, 187, 152, 141, 108, 81, 107, 107, 70, 50 and 48, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [73, 96, 128, 171, 220, 269, 311, 327, 327, 319, 304, 291]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 11 in San Diego, California, USA, along the I805-S freeway, lane 5, direction of southbound. - Today's weather: Fog. Temperature is 13.8\\u00b0C, and visibility reaches 4.5 miles. - Region information: including transportation areas, educational areas and residential areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 391, 368, 346, 272, 234, 188, 144, 127, 118, 92, 65 and 43, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [35, 57, 96, 105, 145, 192, 244, 306, 342, 370, 376, 359]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 11 in San Diego, California, USA, along the I805-S freeway, lane 4, direction of southbound. - Today's weather: Fog. Temperature is 13.8\\u00b0C, and visibility reaches 4.5 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 292, 289, 244, 190, 169, 134, 104, 83, 78, 74, 49 and 35, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [31, 55, 73, 94, 136, 184, 235, 275, 296, 290, 293, 280]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 12 in Orange, California, USA, along the I5-S freeway, lane 5, direction of southbound. - Today's weather: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.0 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 553, 521, 471, 413, 374, 325, 225, 353, 436, 259, 151 and 106, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [108, 127, 158, 203, 282, 372, 498, 536, 572, 538, 564, 534]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 12 in Orange, California, USA, along the I5-S freeway, lane 4, direction of southbound. - Today's weather: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.4 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 471, 448, 432, 365, 323, 276, 210, 230, 309, 212, 146 and 112, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [118, 144, 180, 234, 276, 345, 396, 434, 458, 449, 468, 449]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 12 in Orange, California, USA, along the SR55-N freeway, lane 4, direction of northbound. - Today's weather: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.0 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 418, 393, 308, 284, 240, 217, 162, 201, 208, 151, 83 and 49, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [59, 79, 108, 132, 182, 236, 296, 340, 351, 370, 379, 381]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 12 in Orange, California, USA, along the SR57-S freeway, lane 6, direction of southbound. - Today's weather: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.0 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 499, 484, 416, 347, 292, 223, 167, 232, 234, 152, 92 and 76, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [81, 96, 126, 165, 237, 305, 396, 458, 478, 473, 460, 428]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 12 in Orange, California, USA, along the SR73-N freeway, lane 3, direction of northbound. - Today's weather: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.0 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 60, 55, 36, 28, 20, 15, 11, 14, 13, 6, 5 and 3, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [6, 11, 18, 27, 35, 54, 75, 88, 81, 90, 74, 70]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 12 in Orange, California, USA, along the SR73-S freeway, lane 4, direction of southbound. - Today's weather: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.0 miles. - Region information: including educational areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 113, 87, 79, 72, 61, 40, 18, 12, 6, 4, 2 and 6, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [20, 49, 122, 151, 118, 93, 102, 113, 127, 162, 220, 340]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 12 in Orange, California, USA, along the SR73-S freeway, lane 3, direction of southbound. - Today's weather: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.0 miles. - Region information: including transportation areas, residential areas and educational areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 98, 80, 56, 37, 26, 24, 17, 22, 23, 13, 7 and 4, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [5, 10, 19, 22, 32, 52, 71, 78, 81, 87, 84, 72]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 12 in Orange, California, USA, along the SR91-W freeway, lane 5, direction of westbound. - Today's weather: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.0 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 483, 446, 408, 351, 293, 261, 206, 278, 354, 238, 158 and 84, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [88, 118, 181, 231, 292, 348, 405, 431, 426, 419, 403, 382]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 12 in Orange, California, USA, along the I405-S freeway, lane 5, direction of southbound. - Today's weather: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.0 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 4 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 422, 394, 308, 262, 225, 197, 146, 187, 210, 157, 116 and 104, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 5 AM to 4 PM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [118, 135, 152, 172, 218, 267, 321, 358, 380, 404, 408, 390]}" } ]

Subsets and Splits

No community queries yet

The top public SQL queries from the community will appear here once available.