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Evolutionary dynamics of the last mile travel choice

Author

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  • Li, Qiaoru
  • Wang, Yuanyuan
  • Li, Kun
  • Chen, Liang
  • Wei, Zhenlin

Abstract

The last mile issue is a hot topic in traffic engineering, corresponding to how to complete the last part of a long journey. In this issue, taxis, buses and shared bikes, which are three different ways for individuals to choose, will have a significant impact on their personal utility. Firstly, we quantified the perceived cost of users. According to the characteristics of different travel modes, taking pedestrian perception and travel cost into account, an evolutionary game model of travel mode selection was established based on the replication dynamic equation. By calculating the stable equilibrium state of the system with different cost parameters, how travel modes evolve in infinite populations was analyzed. Secondly, the scene of finite populations was investigated on complex network for comparison. Both results show that when the system reaches a stable equilibrium state, the proportion of the three modes of travel is very sensitive to the change in travel cost. Through this study, we hope to provide guidance for traffic planners to formulate traffic policies and solve traffic congestion in connection problems.

Suggested Citation

  • Li, Qiaoru & Wang, Yuanyuan & Li, Kun & Chen, Liang & Wei, Zhenlin, 2019. "Evolutionary dynamics of the last mile travel choice," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    • Handle: RePEc:eee:phsmap:v:536:y:2019:i:c:s0378437119314608
    DOI: 10.1016/j.physa.2019.122555
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    3. Chen, Liang & Sun, Jingjie & Li, Kun & Li, Qiaoru, 2022. "Research on the effectiveness of monitoring mechanism for “yield to pedestrian” based on system dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    4. Zheyin Jin & Ye Li & Dominique Gruyer & Meiting Tu, 2024. "Enhancing the Carbon Reduction Potential in Ridesplitting through Evolutionary Game Strategies of Tripartite Stakeholders under Carbon-Inclusive Policy," Energies, MDPI, vol. 17(16), pages 1-21, August.
    5. Hua, Mingzhuang & Chen, Xuewu & Chen, Jingxu & Huang, Di & Cheng, Long, 2022. "Large-scale dockless bike sharing repositioning considering future usage and workload balance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).

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