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Social dilemma structure hidden behind traffic flow with route selection

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  • Tanimoto, Jun
  • Nakamura, Kousuke

Abstract

Several traffic flows contain social dilemma structures. Herein, we explored a route-selection problem using a cellular automaton simulation dovetailed with evolutionary game theory. In our model, two classes of driver-agents coexist: D agents (defective strategy), which refer to traffic information for route selection to move fast, and C agents (cooperative strategy), which are insensitive to information and less inclined to move fast. Although no evidence suggests that the social dilemma structure in low density causes vehicles to move freely and that in high density causes traffic jams, we found a structure that corresponds to an n-person (multiplayer) Chicken (n-Chicken) game if the provided traffic information is inappropriate. If appropriate traffic information is given to the agents, the n-Chicken game can be solved. The information delivered to vehicles is crucial for easing the social dilemma due to urban traffic congestion when developing technologies to support the intelligent transportation system (ITS).

Suggested Citation

  • Tanimoto, Jun & Nakamura, Kousuke, 2016. "Social dilemma structure hidden behind traffic flow with route selection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 459(C), pages 92-99.
    • Handle: RePEc:eee:phsmap:v:459:y:2016:i:c:p:92-99
    DOI: 10.1016/j.physa.2016.04.023
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Hossain, Md. Anowar & Tanimoto, Jun, 2022. "A microscopic traffic flow model for sharing information from a vehicle to vehicle by considering system time delay effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    2. Sueyoshi, Fumi & Utsumi, Shinobu & Tanimoto, Jun, 2022. "Underlying social dilemmas in mixed traffic flow with lane changes," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    3. Zhou, Bo & Song, Qiankun & Zhao, Zhenjiang & Liu, Tangzhi, 2020. "A reinforcement learning scheme for the equilibrium of the in-vehicle route choice problem based on congestion game," Applied Mathematics and Computation, Elsevier, vol. 371(C).
    4. Li, Xue-yan & Li, Xue-mei & Yang, Lingrun & Li, Jing, 2018. "Dynamic route and departure time choice model based on self-adaptive reference point and reinforcement learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 77-92.
    5. Li, Xueyan & Qiu, Heting & Yang, Yanni & Zhang, Hankun, 2022. "Differentiated fares depend on bus line and time for urban public transport network based on travelers’ day-to-day group behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    6. Simão, Ricardo, 2021. "Evolution of behaviors in heterogeneous traffic models as driven annealed disorders and its relation to the n-vector model," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
    7. Zhang, Qianran & Ma, Shoufeng & Tian, Junfang & Rose, John M. & Jia, Ning, 2022. "Mode choice between autonomous vehicles and manually-driven vehicles: An experimental study of information and reward," Transportation Research Part A: Policy and Practice, Elsevier, vol. 157(C), pages 24-39.
    8. Simão, Ricardo & Wardil, Lucas, 2021. "Social dilemma in traffic with heterogeneous drivers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).
    9. Jie, Yingmo & Liu, Charles Zhechao & Li, Mingchu & Choo, Kim-Kwang Raymond & Chen, Ling & Guo, Cheng, 2020. "Game theoretic resource allocation model for designing effective traffic safety solution against drunk driving," Applied Mathematics and Computation, Elsevier, vol. 376(C).
    10. Wang, Yongjie & Yao, Zhouzhou & Wang, Chao & Ren, Jiale & Chen, Qiao, 2020. "The impact of intelligent transportation points system based on Elo rating on emergence of cooperation at Y intersection," Applied Mathematics and Computation, Elsevier, vol. 370(C).
    11. Tanimoto, Jun & An, Xie, 2019. "Improvement of traffic flux with introduction of a new lane-change protocol supported by Intelligent Traffic System," Chaos, Solitons & Fractals, Elsevier, vol. 122(C), pages 1-5.
    12. Li, Xue-yan & Li, Xue-mei & Li, Xue-wei & Qiu, He-ting, 2017. "Multi-agent fare optimization model of two modes problem and its analysis based on edge of chaos," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 405-419.
    13. Tanimoto, Jun & Futamata, Masanori & Tanaka, Masaki, 2020. "Automated vehicle control systems need to solve social dilemmas to be disseminated," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).

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