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Improvement of traffic flux with introduction of a new lane-change protocol supported by Intelligent Traffic System

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  • Tanimoto, Jun
  • An, Xie

Abstract

A new Cellular Automata traffic model based on Revised S-NFS model was established, which considers traffic density ahead of a car in next 50 [m] and also accounts for a decision making process of whether a lane change should be tried or not so as to diminish the frequency of meaningless lane-changes. It intends to be applied as one of the protocols to improve traffic efficiency in premise with Intelligence Traffic System (ITS) that is able to provide information on traffic density next hundred meters in front of a focal vehicle. A series of systematic simulations reveals that the presented lane changing protocol enhances traffic flux vis-à-vis the conventional lane change rule based on the traditional incentive criterion and safe criterion. Social dilemma analysis suggests our new protocol mitigates a strong social dilemma encouraged by a competition between a cooperator; not intending any lane-changes and a defector; trying to lane-changes to minimize his own travel time.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:chsofr:v:122:y:2019:i:c:p:1-5
    DOI: 10.1016/j.chaos.2019.03.007
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    References listed on IDEAS

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    1. Shinji Kukida & Jun Tanimoto & Aya Hagishima, 2011. "Analysis Of The Influence Of Lane Changing On Traffic-Flow Dynamics Based On The Cellular Automaton Model," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 22(03), pages 271-281.
    2. Nakata, Makoto & Yamauchi, Atsuo & Tanimoto, Jun & Hagishima, Aya, 2010. "Dilemma game structure hidden in traffic flow at a bottleneck due to a 2 into 1 lane junction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(23), pages 5353-5361.
    3. 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.
    4. Zheng, Zuduo, 2014. "Recent developments and research needs in modeling lane changing," Transportation Research Part B: Methodological, Elsevier, vol. 60(C), pages 16-32.
    5. Zhen Wang & Marko Jusup & Lei Shi & Joung-Hun Lee & Yoh Iwasa & Stefano Boccaletti, 2018. "Exploiting a cognitive bias promotes cooperation in social dilemma experiments," Nature Communications, Nature, vol. 9(1), pages 1-7, December.
    6. Kokubo, Satoshi & Tanimoto, Jun & Hagishima, Aya, 2011. "A new Cellular Automata Model including a decelerating damping effect to reproduce Kerner’s three-phase theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(4), pages 561-568.
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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. Shang, Xue-Cheng & Li, Xin-Gang & Xie, Dong-Fan & Jia, Bin & Jiang, Rui & Liu, Feng, 2022. "A data-driven two-lane traffic flow model based on cellular automata," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    3. Sueyoshi, Fumi & Utsumi, Shinobu & Tanimoto, Jun, 2022. "Underlying social dilemmas in mixed traffic flow with lane changes," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    4. 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).
    5. 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.
    6. Qigang Zhu & Yifan Liu & Ming Liu & Shuaishuai Zhang & Guangyang Chen & Hao Meng, 2021. "Intelligent Planning and Research on Urban Traffic Congestion," Future Internet, MDPI, vol. 13(11), pages 1-17, November.
    7. Dong, Yuming & Jia, Xiaolu & Yanagisawa, Daichi & Nagahama, Akihito & Nishinari, Katsuhiro, 2022. "Optimization of transition behaviors in a two-lane system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P2).
    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. 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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