中国物理B ›› 2011, Vol. 20 ›› Issue (2): 28901-028901.doi: 10.1088/1674-1056/20/2/028901

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

A traffic flow cellular automaton model to considering drivers' learning and forgetting behaviour

丁建勋, 黄海军, 田琼   

  1. School of Economics and Management, Beihang University, Beijing 100191, China
  • 收稿日期:2010-07-26 修回日期:2010-08-30 出版日期:2011-02-15 发布日期:2011-02-15
  • 基金资助:
    Project supported by the National Natural Science Foundation of China (Grant No. 70821061) and the National Basic Research Program of China (Grant No. 2006CB705503).

A traffic flow cellular automaton model to considering drivers' learning and forgetting behaviour

Ding Jian-Xun(丁建勋), Huang Hai-Jun(黄海军)†, and Tian Qiong(田琼)   

  1. School of Economics and Management, Beihang University, Beijing 100191, China
  • Received:2010-07-26 Revised:2010-08-30 Online:2011-02-15 Published:2011-02-15
  • Supported by:
    Project supported by the National Natural Science Foundation of China (Grant No. 70821061) and the National Basic Research Program of China (Grant No. 2006CB705503).

PDF (PC)

1066

赞

摘要： It is known that the commonly used NaSch cellular automaton (CA) model and its modifications can help explain the internal causes of the macro phenomena of traffic flow. However, the randomization probability of vehicle velocity used in these models is assumed to be an exogenous constant or a conditional constant, which cannot reflect the learning and forgetting behaviour of drivers with historical experiences. This paper further modifies the NaSch model by enabling the randomization probability to be adjusted on the bases of drivers' memory. The Markov properties of this modified model are discussed. Analytical and simulation results show that the traffic fundamental diagrams can be indeed improved when considering drivers' intelligent behaviour. Some new features of traffic are revealed by differently combining the model parameters representing learning and forgetting behaviour.

Abstract: It is known that the commonly used NaSch cellular automaton (CA) model and its modifications can help explain the internal causes of the macro phenomena of traffic flow. However, the randomization probability of vehicle velocity used in these models is assumed to be an exogenous constant or a conditional constant, which cannot reflect the learning and forgetting behaviour of drivers with historical experiences. This paper further modifies the NaSch model by enabling the randomization probability to be adjusted on the bases of drivers' memory. The Markov properties of this modified model are discussed. Analytical and simulation results show that the traffic fundamental diagrams can be indeed improved when considering drivers' intelligent behaviour. Some new features of traffic are revealed by differently combining the model parameters representing learning and forgetting behaviour.

Key words: cellular automaton model, learning and forgetting behaviour, Markov property

中图分类号:  (Transportation)

  • 89.40.-a
45.70.Vn (Granular models of complex systems; traffic flow)