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Predictive Cordon Pricing to Reduce Air Pollution

Vosough, Shaghayegh Sadat | 2021

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  1. Type of Document: Ph.D. Dissertation
  2. Language: Farsi
  3. Document No: 54135 (09)
  4. University: Sharif University of Technology
  5. Department: Civil Engineering
  6. Advisor(s): Poorzahedy, Hossain; Lindsey, Robin
  7. Abstract:
  8. Traffic is a major contributor to emissions in many large cities with severe air pollution. Experience in London, Milan, and Stockholm shows that charging for the use of roads can be effective in reducing emissions, as well as congestion. This study examines the use of predictive cordon tolls based on weather forecasts to reduce ambient air pollution and congestion. Travelers choose their destinations inside or outside the cordon, and whether to drive or take public transport. Passenger vehicles are divided into three classes according to their emission characteristics, and higher tolls are imposed on more polluting vehicles. The Box model of emission dispersion is used to predict air quality. A Markov decision-making process then determines daily toll levels with the objective of maximizing welfare measured by travelers’ surplus, toll revenue, and air pollution health costs. The model is applied to two hypothetical networks using recorded weather data for Tehran in 2016. With base-case parameter values, predictive pricing reduces the daily average CO concentration as well as the number of days with dangerous air quality. Predictive pricing yields a higher welfare gain than a fixed toll (i.e., the same toll every day regardless of weather conditions). The effects of weather information, wind forecast accuracy, forecast time horizon, values of travel time, destination attractions, and road link capacity on the benefits from predictive pricing are analyzed. The performance of the model under randomized weather conditions is also assessed
  9. Keywords:
  10. Air Pollution ; Multiclass Traffic Assignment ; Markov Model ; Box Scheme ; Predictive Toll ; Sustainable Cities ; Transportation Demand Management ; Dynamical Traffic Assignment

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