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A Bi-Level Optimization Approach to Network Flow Management Incorporating Travelers’ Herd Effect

Author

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  • Shihao Li

    (School of Transportation, Southeast University, Nanjing 210096, China)

  • Bojian Zhou

    (School of Transportation, Southeast University, Nanjing 210096, China)

  • Min Xu

    (Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China)

  • Xiaoxiao Dong

    (School of Transportation, Southeast University, Nanjing 210096, China)

Abstract

Herd effect is a widespread phenomenon in real-world situations. This study explores how the herd effect can be used to manage network flow effectively. We examined its impact on travelers’ route choices and propose a mixed network flow evolution process that incorporates the herd effect, considering two types of travelers: those who receive route subsidy information and those who do not. Based on this evolution process, we developed a bi-level optimization model to determine the optimal subsidized routes, the subsidy amounts per kilometer, and the proportion of travelers receiving subsidy information. A hybrid algorithm with two iterative procedures was proposed to solve the model, in which the adaptive genetic algorithm (AGA) was employed to solve the upper-level nonlinear mixed-integer programming problem, and the partial linearization method was used to solve the lower-level network flow evolution process. Numerical results indicate that the presence of herd effect can effectively reduce both the total travel time of the network and the overall subsidy costs. The findings of this study have significant implications for the utilization of the herd effect in designing navigation software and developing congestion pricing strategies.

Suggested Citation

  • Shihao Li & Bojian Zhou & Min Xu & Xiaoxiao Dong, 2024. "A Bi-Level Optimization Approach to Network Flow Management Incorporating Travelers’ Herd Effect," Mathematics, MDPI, vol. 12(24), pages 1-29, December.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:24:p:3923-:d:1542826
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    References listed on IDEAS

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    1. Laurens Rook, 2006. "An Economic Psychological Approach to Herd Behavior," Journal of Economic Issues, Taylor & Francis Journals, vol. 40(1), pages 75-95, March.
    2. Manca, Francesco & Sivakumar, Aruna & Polak, John W., 2022. "Capturing the effect of multiple social influence sources on the adoption of new transport technologies and services," Journal of choice modelling, Elsevier, vol. 42(C).
    3. William H. Sandholm, 2002. "Evolutionary Implementation and Congestion Pricing," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 69(3), pages 667-689.
    4. Zangui, Mahmood & Aashtiani, Hedayat Z. & Lawphongpanich, Siriphong & Yin, Yafeng, 2015. "Path-differentiated pricing in congestion mitigation," Transportation Research Part B: Methodological, Elsevier, vol. 80(C), pages 202-219.
    5. Han, Linghui & Wang, David Z.W. & Lo, Hong K. & Zhu, Chengjuan & Cai, Xingju, 2017. "Discrete-time day-to-day dynamic congestion pricing scheme considering multiple equilibria," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 1-16.
    6. Luan, Mingye & Waller, S.Travis & Rey, David, 2023. "A non-additive path-based reward credit scheme for traffic congestion management," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    7. Zhang, Fang & Lu, Jian & Hu, Xiaojian, 2022. "Integrated path controlling and subsidy scheme for mobility and environmental management in automated transportation networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 167(C).
    8. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(3), pages 797-817.
    9. Ye, Hongbo & Yang, Hai & Tan, Zhijia, 2015. "Learning marginal-cost pricing via a trial-and-error procedure with day-to-day flow dynamics," Transportation Research Part B: Methodological, Elsevier, vol. 81(P3), pages 794-807.
    10. Lei Bo & Zihang Zhang & Yang Liu & Shangqing Yang & Yanwen Wang & Yiying Wang & Xuanrui Zhang, 2024. "Research on Path Planning Method of Solid Backfilling and Pushing Mechanism Based on Adaptive Genetic Particle Swarm Optimization," Mathematics, MDPI, vol. 12(3), pages 1-27, February.
    11. Lo, Hong K. & Szeto, W. Y., 2002. "A methodology for sustainable traveler information services," Transportation Research Part B: Methodological, Elsevier, vol. 36(2), pages 113-130, February.
    12. Wie, Byung-Wook & Tobin, Roger L., 1998. "Dynamic congestion pricing models for general traffic networks," Transportation Research Part B: Methodological, Elsevier, vol. 32(5), pages 313-327, June.
    13. Kim, Jinhee & Rasouli, Soora & Timmermans, Harry, 2014. "Expanding scope of hybrid choice models allowing for mixture of social influences and latent attitudes: Application to intended purchase of electric cars," Transportation Research Part A: Policy and Practice, Elsevier, vol. 69(C), pages 71-85.
    14. Qixiu Cheng & Jiping Xing & Wendy Yi & Zhiyuan Liu & Xiao Fu, 2019. "Distance-Based Congestion Pricing with Day-to-Day Dynamic Traffic Flow Evolution Process," Discrete Dynamics in Nature and Society, Hindawi, vol. 2019, pages 1-7, January.
    15. Sherwin, Henrietta & Chatterjee, Kiron & Jain, Juliet, 2014. "An exploration of the importance of social influence in the decision to start bicycling in England," Transportation Research Part A: Policy and Practice, Elsevier, vol. 68(C), pages 32-45.
    16. Shlomo Bekhor & Moshe Ben-Akiva & M. Ramming, 2006. "Evaluation of choice set generation algorithms for route choice models," Annals of Operations Research, Springer, vol. 144(1), pages 235-247, April.
    17. Yang, Hai & Huang, Hai-Jun, 2004. "The multi-class, multi-criteria traffic network equilibrium and systems optimum problem," Transportation Research Part B: Methodological, Elsevier, vol. 38(1), pages 1-15, January.
    18. Wu, Jiyan & Tian, Ye & Sun, Jian & Michael Zhang, H. & Wang, Yunpeng, 2023. "Public or private? Optimal organization for incentive-based travel demand management," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 174(C).
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