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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? | 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°C, and visibility reaches 7.4 miles.
- Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.
- Current Time: 0 AM, 2018-1-1, Monday, New Year's Day.
- Traffic volume data in the past 12 hours were 196, 208, 204, 174, 215, 176, 155, 126, 97, 70, 58 and 38, respectively.
According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 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] | {Traffic volume data in the next 12 hours: [53, 50, 40, 34, 28, 42, 46, 58, 90, 125, 170, 198]} |
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? | 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°C, and visibility reaches 8.1 miles.
- Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.
- Current Time: 0 AM, 2018-1-1, Monday, New Year's Day.
- Traffic volume data in the past 12 hours were 294, 273, 273, 254, 240, 232, 208, 167, 168, 152, 98 and 110, respectively.
According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 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] | {Traffic volume data in the next 12 hours: [101, 69, 45, 30, 48, 60, 89, 110, 140, 177, 228, 265]} |
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? | 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°C, and visibility reaches 8.1 miles.
- Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.
- Current Time: 0 AM, 2018-1-1, Monday, New Year's Day.
- Traffic volume data in the past 12 hours were 202, 200, 207, 188, 170, 152, 122, 100, 83, 75, 50 and 48, respectively.
According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 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] | {Traffic volume data in the next 12 hours: [44, 27, 19, 15, 26, 34, 46, 57, 88, 116, 142, 158]} |
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? | 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°C, and visibility reaches 7.4 miles.
- Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.
- Current Time: 0 AM, 2018-1-1, Monday, New Year's Day.
- Traffic volume data in the past 12 hours were 239, 236, 221, 194, 177, 176, 142, 114, 110, 79, 48 and 62, respectively.
According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 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] | {Traffic volume data in the next 12 hours: [62, 40, 27, 38, 36, 47, 50, 84, 139, 212, 246, 274]} |
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? | 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°C, and visibility reaches 8.1 miles.
- Region information: including transportation areas and commercial areas within a range of 5 km.
- Current Time: 0 AM, 2018-1-1, Monday, New Year's Day.
- Traffic volume data in the past 12 hours were 136, 130, 130, 132, 122, 104, 74, 62, 52, 51, 42 and 54, respectively.
According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 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] | {Traffic volume data in the next 12 hours: [50, 41, 29, 30, 30, 33, 40, 62, 98, 128, 152, 154]} |
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? | 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°C, and visibility reaches 7.4 miles.
- Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.
- Current Time: 0 AM, 2018-1-1, Monday, New Year's Day.
- Traffic volume data in the past 12 hours were 520, 582, 535, 394, 358, 330, 320, 259, 248, 216, 156 and 181, respectively.
According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 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] | {Traffic volume data in the next 12 hours: [164, 124, 73, 62, 59, 78, 104, 129, 224, 340, 411, 461]} |
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? | 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°C, and visibility reaches 8.1 miles.
- Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.
- Current Time: 0 AM, 2018-1-1, Monday, New Year's Day.
- Traffic volume data in the past 12 hours were 276, 280, 273, 252, 236, 244, 232, 192, 193, 175, 120 and 134, respectively.
According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 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] | {Traffic volume data in the next 12 hours: [141, 96, 50, 34, 34, 55, 73, 85, 128, 168, 193, 241]} |
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? | 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°C, and visibility reaches 8.1 miles.
- Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.
- Current Time: 0 AM, 2018-1-1, Monday, New Year's Day.
- Traffic volume data in the past 12 hours were 316, 323, 334, 312, 312, 296, 260, 235, 198, 166, 134 and 135, respectively.
According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 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] | {Traffic volume data in the next 12 hours: [146, 118, 85, 68, 74, 94, 94, 110, 167, 220, 264, 299]} |
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? | 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°C, and visibility reaches 8.1 miles.
- Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.
- Current Time: 0 AM, 2018-1-1, Monday, New Year's Day.
- Traffic volume data in the past 12 hours were 297, 289, 271, 256, 242, 218, 176, 147, 135, 126, 107 and 113, respectively.
According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 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] | {Traffic volume data in the next 12 hours: [112, 97, 92, 86, 93, 106, 114, 144, 193, 242, 282, 312]} |
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? | 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°C, and visibility reaches 7.4 miles.
- Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.
- Current Time: 0 AM, 2018-1-1, Monday, New Year's Day.
- Traffic volume data in the past 12 hours were 357, 356, 349, 327, 298, 275, 242, 222, 196, 167, 113 and 123, respectively.
According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 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] | {Traffic volume data in the next 12 hours: [125, 83, 60, 50, 60, 84, 88, 116, 167, 214, 255, 289]} |
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? | 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°C, and visibility reaches 7.4 miles.
- Region information: including transportation areas, residential areas and educational areas within a range of 5 km.
- Current Time: 0 AM, 2018-1-1, Monday, New Year's Day.
- Traffic volume data in the past 12 hours were 136, 136, 142, 128, 116, 101, 82, 62, 62, 57, 36 and 52, respectively.
According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 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] | {Traffic volume data in the next 12 hours: [52, 35, 24, 17, 18, 22, 31, 44, 62, 92, 114, 119]} |
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? | 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°C, and visibility reaches 9.3 miles.
- Region information: including transportation areas within a range of 5 km.
- Current Time: 0 AM, 2018-1-1, Monday, New Year's Day.
- Traffic volume data in the past 12 hours were 93, 92, 92, 82, 81, 67, 55, 46, 33, 22, 16 and 17, respectively.
According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 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] | {Traffic volume data in the next 12 hours: [25, 16, 14, 20, 18, 22, 28, 34, 54, 68, 90, 89]} |
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? | 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°C, and visibility reaches 7.4 miles.
- Region information: including transportation areas and commercial areas within a range of 5 km.
- Current Time: 0 AM, 2018-1-1, Monday, New Year's Day.
- Traffic volume data in the past 12 hours were 342, 339, 333, 350, 357, 340, 269, 228, 204, 179, 105 and 77, respectively.
According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 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] | {Traffic volume data in the next 12 hours: [114, 29, 1, 3, 31, 80, 22, 108, 164, 228, 295, 339]} |
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? | 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°C, and visibility reaches 7.4 miles.
- Region information: including transportation areas, educational areas and residential areas within a range of 5 km.
- Current Time: 0 AM, 2018-1-1, Monday, New Year's Day.
- Traffic volume data in the past 12 hours were 300, 307, 298, 280, 262, 256, 213, 190, 162, 121, 74 and 42, respectively.
According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 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] | {Traffic volume data in the next 12 hours: [25, 20, 18, 28, 79, 156, 244, 256, 228, 229, 250, 276]} |
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? | 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°C, and visibility reaches 5.0 miles.
- Region information: including transportation areas, residential areas and educational areas within a range of 5 km.
- Current Time: 0 AM, 2018-1-1, Monday, New Year's Day.
- Traffic volume data in the past 12 hours were 206, 220, 230, 252, 240, 238, 163, 122, 94, 72, 54 and 64, respectively.
According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 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] | {Traffic volume data in the next 12 hours: [74, 42, 25, 20, 28, 32, 41, 60, 91, 136, 190, 224]} |
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? | 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°C, and visibility reaches 7.6 miles.
- Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.
- Current Time: 0 AM, 2018-1-1, Monday, New Year's Day.
- Traffic volume data in the past 12 hours were 489, 482, 471, 475, 470, 498, 464, 426, 411, 339, 220 and 147, respectively.
According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 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] | {Traffic volume data in the next 12 hours: [177, 124, 106, 96, 105, 146, 151, 196, 253, 330, 402, 464]} |
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? | 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°C, and visibility reaches 7.4 miles.
- Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.
- Current Time: 0 AM, 2018-1-1, Monday, New Year's Day.
- Traffic volume data in the past 12 hours were 286, 296, 302, 284, 278, 235, 208, 177, 176, 169, 144 and 221, respectively.
According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 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] | {Traffic volume data in the next 12 hours: [259, 184, 126, 89, 85, 92, 121, 158, 197, 254, 298, 334]} |
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? | 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°C, and visibility reaches 5.0 miles.
- Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.
- Current Time: 0 AM, 2018-1-1, Monday, New Year's Day.
- Traffic volume data in the past 12 hours were 220, 223, 220, 213, 201, 193, 164, 148, 130, 97, 62 and 35, respectively.
According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 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] | {Traffic volume data in the next 12 hours: [18, 15, 14, 25, 66, 118, 176, 180, 176, 175, 185, 196]} |
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? | 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°C, and visibility reaches 7.4 miles.
- Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.
- Current Time: 0 AM, 2018-1-1, Monday, New Year's Day.
- Traffic volume data in the past 12 hours were 249, 291, 286, 286, 280, 247, 210, 196, 172, 154, 137 and 113, respectively.
According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 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] | {Traffic volume data in the next 12 hours: [120, 106, 93, 84, 92, 119, 125, 137, 174, 228, 281, 309]} |
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? | 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°C, and visibility reaches 7.6 miles.
- Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.
- Current Time: 0 AM, 2018-1-1, Monday, New Year's Day.
- Traffic volume data in the past 12 hours were 379, 385, 396, 395, 368, 356, 310, 269, 230, 208, 145 and 120, respectively.
According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 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] | {Traffic volume data in the next 12 hours: [134, 82, 56, 44, 49, 68, 92, 117, 173, 248, 321, 367]} |
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? | 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°C, and visibility reaches 7.4 miles.
- Region information: including transportation areas and commercial areas within a range of 5 km.
- Current Time: 0 AM, 2018-1-1, Monday, New Year's Day.
- Traffic volume data in the past 12 hours were 400, 409, 397, 373, 349, 342, 285, 253, 216, 160, 99 and 56, respectively.
According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 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] | {Traffic volume data in the next 12 hours: [34, 28, 24, 38, 106, 208, 324, 342, 303, 306, 333, 367]} |
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? | 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°C, and visibility reaches 5.0 miles.
- Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.
- Current Time: 0 AM, 2018-1-1, Monday, New Year's Day.
- Traffic volume data in the past 12 hours were 211, 217, 208, 211, 202, 196, 167, 146, 126, 106, 90 and 124, respectively.
According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 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] | {Traffic volume data in the next 12 hours: [132, 92, 71, 66, 64, 78, 88, 112, 144, 171, 205, 217]} |
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? | 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°C, and visibility reaches 7.4 miles.
- Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.
- Current Time: 0 AM, 2018-1-1, Monday, New Year's Day.
- Traffic volume data in the past 12 hours were 592, 605, 585, 567, 526, 505, 448, 390, 336, 300, 292 and 276, respectively.
According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 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] | {Traffic volume data in the next 12 hours: [353, 277, 221, 147, 118, 153, 193, 223, 288, 384, 481, 530]} |
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? | 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°C, and visibility reaches 5.0 miles.
- Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.
- Current Time: 0 AM, 2018-1-1, Monday, New Year's Day.
- Traffic volume data in the past 12 hours were 324, 314, 326, 334, 314, 292, 254, 205, 168, 134, 63 and 94, respectively.
According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 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] | {Traffic volume data in the next 12 hours: [131, 77, 10, 1, 1, 1, 6, 33, 125, 201, 266, 310]} |
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? | 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°C, and visibility reaches 7.4 miles.
- Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.
- Current Time: 0 AM, 2018-1-1, Monday, New Year's Day.
- Traffic volume data in the past 12 hours were 286, 291, 292, 291, 287, 263, 224, 195, 156, 134, 100 and 124, respectively.
According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 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] | {Traffic volume data in the next 12 hours: [134, 101, 82, 64, 63, 73, 90, 117, 154, 204, 248, 272]} |
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