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The study of traffic flow model based on cellular automata and Naive Bayes

Author

Listed:
  • Hong Zhang

    (College of Information Science and Engineering, Chengdu University, Chengdu 610106, P. R. China†Key Laboratory of Pattern Recognition and Intelligent, Information Processing, Chengdu 610106, P. R. China)

  • Juan Wei

    (#x2021;College of Computer Science, Chengdu Normal University, Chengdu 611130, P. R. China§Institute of Intelligent Computing and Information Technology, Chengdu Normal University, Chengdu 611130, P. R. China)

  • Xiaoling Gao

    (#x2021;College of Computer Science, Chengdu Normal University, Chengdu 611130, P. R. China)

  • Jun Hu

    (#x2021;College of Computer Science, Chengdu Normal University, Chengdu 611130, P. R. China§Institute of Intelligent Computing and Information Technology, Chengdu Normal University, Chengdu 611130, P. R. China)

Abstract

A new traffic flow model is proposed based on cellular automata and Naive Bayes theory to effectively describe the traffic flow velocity and flow state of the road. On the basis of NaSch model, the safety distance is fully considered in this model, and random deceleration and inflow rules of a vehicle are introduced. At the same time, vehicle acceleration, deceleration and lane change are optimized with Naive Bayes theory. Finally, experimental platform is used for numerical analysis, and the relationship between such parameters as average velocity of traffic flow, maximum velocity of traffic flow, number of inflow vehicles and random deceleration probability, etc. is studied in depth. The results show that the maximum velocity has a great effect on the traffic flow state, and when the vehicle inflow probability is lower, random deceleration probability has less effect on the average velocity and the number of vehicles waiting for inflow; on the contrary, the higher the random deceleration probability is, the more obvious the tendency of road congestion.

Suggested Citation

  • Hong Zhang & Juan Wei & Xiaoling Gao & Jun Hu, 2019. "The study of traffic flow model based on cellular automata and Naive Bayes," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 30(05), pages 1-16, May.
    • Handle: RePEc:wsi:ijmpcx:v:30:y:2019:i:05:n:s0129183119500347
    DOI: 10.1142/S0129183119500347
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    Cited by:

    1. Jiang, Yanqun & Ding, Zhongjun & Zhou, Jun & Wu, Peng & Chen, Bokui, 2022. "Estimation of traffic emissions in a polycentric urban city based on a macroscopic approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 602(C).

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