中国物理B ›› 2016, Vol. 25 ›› Issue (10): 108902-108902.doi: 10.1088/1674-1056/25/10/108902

• INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY • 上一篇    下一篇

A new cellular automata model of traffic flow with negative exponential weighted look-ahead potential

Xiao Ma(马骁), Wei-Fan Zheng(郑伟范), Bao-Shan Jiang(江宝山), Ji-Ye Zhang(张继业)   

  1. 1 Traction Power State Key Laboratory, Southwest Jiaotong University, Chengdu 610031, China;
    2 School of Mathematics, Southwest Jiaotong University, Chengdu 610031, China;
    3 Information Research Institute, Southwest Jiaotong University, Chengdu 610031, China
  • 收稿日期:2016-03-21 修回日期:2016-05-22 出版日期:2016-10-05 发布日期:2016-10-05
  • 通讯作者: Bao-Shan Jiang E-mail:jyzhang@home.swjtu.edu.cn
  • 基金资助:
    Project supported by the National Natural Science Foundation of China (Grant Nos. 11572264, 11172247, 11402214, and 61373009).

A new cellular automata model of traffic flow with negative exponential weighted look-ahead potential

Xiao Ma(马骁)1,2, Wei-Fan Zheng(郑伟范)1,3, Bao-Shan Jiang(江宝山)1, Ji-Ye Zhang(张继业)1   

  1. 1 Traction Power State Key Laboratory, Southwest Jiaotong University, Chengdu 610031, China;
    2 School of Mathematics, Southwest Jiaotong University, Chengdu 610031, China;
    3 Information Research Institute, Southwest Jiaotong University, Chengdu 610031, China
  • Received:2016-03-21 Revised:2016-05-22 Online:2016-10-05 Published:2016-10-05
  • Contact: Bao-Shan Jiang E-mail:jyzhang@home.swjtu.edu.cn
  • Supported by:
    Project supported by the National Natural Science Foundation of China (Grant Nos. 11572264, 11172247, 11402214, and 61373009).

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摘要： With the development of traffic systems, some issues such as traffic jams become more and more serious. Efficient traffic flow theory is needed to guide the overall controlling, organizing and management of traffic systems. On the basis of the cellular automata model and the traffic flow model with look-ahead potential, a new cellular automata traffic flow model with negative exponential weighted look-ahead potential is presented in this paper. By introducing the negative exponential weighting coefficient into the look-ahead potential and endowing the potential of vehicles closer to the driver with a greater coefficient, the modeling process is more suitable for the driver's random decision-making process which is based on the traffic environment that the driver is facing. The fundamental diagrams for different weighting parameters are obtained by using numerical simulations which show that the negative exponential weighting coefficient has an obvious effect on high density traffic flux. The complex high density non-linear traffic behavior is also reproduced by numerical simulations.

关键词: traffic flow, look-ahead potential, cellular automata, negative exponential weighting

Abstract: With the development of traffic systems, some issues such as traffic jams become more and more serious. Efficient traffic flow theory is needed to guide the overall controlling, organizing and management of traffic systems. On the basis of the cellular automata model and the traffic flow model with look-ahead potential, a new cellular automata traffic flow model with negative exponential weighted look-ahead potential is presented in this paper. By introducing the negative exponential weighting coefficient into the look-ahead potential and endowing the potential of vehicles closer to the driver with a greater coefficient, the modeling process is more suitable for the driver's random decision-making process which is based on the traffic environment that the driver is facing. The fundamental diagrams for different weighting parameters are obtained by using numerical simulations which show that the negative exponential weighting coefficient has an obvious effect on high density traffic flux. The complex high density non-linear traffic behavior is also reproduced by numerical simulations.

Key words: traffic flow, look-ahead potential, cellular automata, negative exponential weighting

中图分类号:  (Land transportation)

  • 89.40.Bb
02.50.-r (Probability theory, stochastic processes, and statistics) 05.10.Ln (Monte Carlo methods)