Traffic Emission Prediction scheme (TEPs)

Traffic Emission Prediction scheme (TEPs)Traffic Emission Prediction scheme (TEPs)Traffic Emission Prediction scheme (TEPs)

Traffic Emission Prediction scheme (TEPs)

Traffic Emission Prediction scheme (TEPs)Traffic Emission Prediction scheme (TEPs)Traffic Emission Prediction scheme (TEPs)
  • Home
  • Features
    • TEPs-I: Traffic module
    • TEPs-II: emission module
    • Satellite data for TEPs
  • Applications
    • PECOUNT-I
    • PECOUNT-II
    • PRTCS
    • KCOUNT
    • OptimStation
  • Results
    • Case Studies
  • Publications
  • TEPs in other websites
  • More
    • Home
    • Features
      • TEPs-I: Traffic module
      • TEPs-II: emission module
      • Satellite data for TEPs
    • Applications
      • PECOUNT-I
      • PECOUNT-II
      • PRTCS
      • KCOUNT
      • OptimStation
    • Results
      • Case Studies
    • Publications
    • TEPs in other websites

  • Home
  • Features
    • TEPs-I: Traffic module
    • TEPs-II: emission module
    • Satellite data for TEPs
  • Applications
    • PECOUNT-I
    • PECOUNT-II
    • PRTCS
    • KCOUNT
    • OptimStation
  • Results
    • Case Studies
  • Publications
  • TEPs in other websites

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TEPs and Satellite

TEPs and pollution backcasting

TEPs and Satellite

 This study enhances spatio-temporal interpolation models for Annual  Average Daily Traffic (AADT) estimation across urban roads by  incorporating vehicle detection from aerial images, achieving a high  predictive accuracy of 90% R-squared for daily traffic counts, but  highlighting the importance of longer-term data for accurate AADT  predictions. 

TEPs and structure

TEPs and pollution backcasting

TEPs and Satellite

 The paper introduces a novel structure of TEPS,  acknowledging spatio-temporal relationships and recognizing patterns, to  predict periodic and annual volumes as well as greenhouse gas emissions  at the individual roadway level across a large urban road network,  addressing limitations in traditional emission inventories based on  househ

 The paper introduces a novel structure of TEPS,  acknowledging spatio-temporal relationships and recognizing patterns, to  predict periodic and annual volumes as well as greenhouse gas emissions  at the individual roadway level across a large urban road network,  addressing limitations in traditional emission inventories based on  household travel data and providing a universal approach validated with  City of Toronto traffic count program data 

TEPs and pollution backcasting

TEPs and pollution backcasting

TEPs and pollution backcasting

 This study proposes a method for backcasting traffic-related air  pollution surfaces, incorporating temporal variability in traffic and  emissions using TEPs as well as trends in concentrations measured at reference  stations, revealing spatially heterogeneous improvements in nitrogen  dioxide (NO2) concentrations in the City of Toronto 

 This study proposes a method for backcasting traffic-related air  pollution surfaces, incorporating temporal variability in traffic and  emissions using TEPs as well as trends in concentrations measured at reference  stations, revealing spatially heterogeneous improvements in nitrogen  dioxide (NO2) concentrations in the City of Toronto from 2006 to 2020,  with average decreases of 20.46% and notable variations along major  roads and highways. 

 This project was supported by The City of Toronto's Transportation Services Division  and Atmospheric Fund

 

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Updated on Feb 28, 2019