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Modeling The Railway Traffic Using Cellular Automata Model

Author

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  • KE-PING LI

    (School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, P. R. China)

  • ZI-YOU GAO

    (School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, P. R. China)

  • BIN NING

    (School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, P. R. China)

Abstract

The NaSch model is a probabilistic cellular automaton (CA) model for the description of single-lane highway traffic. Based on this model, we propose a new cellular automata model for the railway traffic. The signaling system adopted in this work is the moving block signaling system. In our model, the distance between the following train and its leading train takes into account the velocity of the leading train. The simulation results demonstrate that our model is suitable for simulating the railway traffic. Compared our scheme to the traditional moving-space-block scheme, it is obvious that using our scheme, not only the line capacity can be increased, but also the traffic flow states are safer.

Suggested Citation

  • Ke-Ping Li & Zi-You Gao & Bin Ning, 2005. "Modeling The Railway Traffic Using Cellular Automata Model," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 16(06), pages 921-932.
    • Handle: RePEc:wsi:ijmpcx:v:16:y:2005:i:06:n:s0129183105007613
    DOI: 10.1142/S0129183105007613
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    References listed on IDEAS

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    1. Qi, Le & Zheng, Zhongyi & Gang, Longhui, 2017. "A cellular automaton model for ship traffic flow in waterways," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 705-717.
    2. Zhang, San-Tong & Chen, Yi-Chuan, 2011. "Simulation for influence of train failure on railway traffic flow and research on train operation adjusting strategies using cellular automata," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(21), pages 3710-3718.
    3. Toru Seo & Kentaro Wada & Daisuke Fukuda, 2023. "Fundamental diagram of urban rail transit considering train–passenger interaction," Transportation, Springer, vol. 50(4), pages 1399-1424, August.
    4. Anupriya, & Graham, Daniel J. & Bansal, Prateek & Hörcher, Daniel & Anderson, Richard, 2023. "Optimal congestion control strategies for near-capacity urban metros: Informing intervention via fundamental diagrams," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    5. Zhou, Yonghua & Tao, Xin & Luan, Lei & Ning, Jingjie, 2018. "Revisiting the 7/23 train accident using computer reconstruction simulation for causation and prevention analysis," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 148(C), pages 1-15.
    6. Qian, Yongsheng & Da, Cheng & Zeng, Junwei & Wang, Xuexin & Zhang, Yongzhi & Xu, Dejie, 2022. "A bidirectional quasi-moving block cellular automaton model for single-track railways," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).

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