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Policy-Combination Oriented Optimization for Public Transportation Based on the Game Theory

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  • Wentao Zhu
  • Mengwei Chen
  • Dianhai Wang
  • Dongfang Ma

Abstract

This research aims at detecting the interactions between policy maker and travelers when making public transport policy and strategies to optimize relevant policy combination. In the two scenarios of whether to set a bus lane or not, travel cost functions of bus and car are proposed, respectively, with the in-vehicle comfort level of passengers considered. By introducing the bottleneck model and transit assignment model, travelers’ behaviors are revealed according to different travel mode. Focusing on minimizing the total cost of the system (TSC), Stackelberg game model is built to describe the dynamic interactions between the government, the bus company, and travelers. Finally, kriging surrogate method is proposed on account of numerical simulation to find solution to the game model and propose the optimal policy combination and resource allocation. The results show an effective performance: under the assumption that the travel distance is 20km, the optimized policy combination can reduce TSC by 8.59% and 9.82% in two scenarios, respectively, and reduce travel cost per person by 10.28% and 15.85%, respectively.

Suggested Citation

  • Wentao Zhu & Mengwei Chen & Dianhai Wang & Dongfang Ma, 2018. "Policy-Combination Oriented Optimization for Public Transportation Based on the Game Theory," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-14, July.
    • Handle: RePEc:hin:jnlmpe:7510279
    DOI: 10.1155/2018/7510279
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