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Evaluating the Impact of Gasoline Price Change on the Passing Car Volume in the Provinces of Iran and Tehran and the Impact of CBD Entry Policy Change on the Passing Car Volume in Tehran

Oshanreh, Mohammad Mehdi | 2022

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 55270 (09)
  4. University: Sharif University of Technology
  5. Department: Civil Engineering
  6. Advisor(s): Amini, Zahra
  7. Abstract:
  8. Nowadays, various policies are adopted by transportation managers and planners. These policies aim to improve system performance, reduce user costs, control and reduce air pollution, reduce noise pollution, and ultimately reduce congestion. A set of these policies in the form of transportation demand management is presented in the literature. A common way to find the effect of a policy on user behavior is to use questionnaires. Other causal inference models have been proposed in disciplines such as statistics, political science, marketing science, epidemiology, and psychology. The purpose of these models is to find the causal effect of an intervention (treatment) on a system. These studies are straightforward and inexpensive based on real-time data. This study aimed to investigate causal inference models and their potential in finding the effects of an intervention (or a policy) in the field of transportation. Regression Discontinuity models, Interrupted Time Series, and Bayesian Structural Time Series models have been used in this study. The online traffic data of the Roads and Transportation Organization of Iran and the data of the license plate camera recognition installed in the CBD area and the air pollution control area of Tehran have been used to find the effect of gasoline price change in the year of 1398 on the daily traffic volume of passenger cars. Also, daily traffic volume of the city of Tehran have been used to find the effect of changing the policy of entering the air pollution control area. By comparing the results of the models, the Bayesian Structural Time Series model have been introduced as an efficient model for causal inference analysis due to the uncertainty in the system and the possibility of considering previous assumptions about the system.
  9. Keywords:
  10. Transportation Demand Management ; Causal Inference ; Regression Discontinuity Design ; Interrupted Time Series Analysis ; Bayesian Structural Time Series ; Gasoline Price

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