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A traffic flow cellular automaton model to considering drivers' learning and forgetting behaviour 被引量:3

A traffic flow cellular automaton model to considering drivers' learning and forgetting behaviour
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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. 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.
出处 《Chinese Physics B》 SCIE EI CAS CSCD 2011年第2期575-585,共11页 中国物理B(英文版)
基金 supported by the National Natural Science Foundation of China (Grant No. 70821061) the National Basic Research Program of China (Grant No. 2006CB705503)
关键词 cellular automaton model learning and forgetting behaviour Markov property cellular automaton model, learning and forgetting behaviour, Markov property
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  • 1Lighthill M J and Whitham G B 1955 Proc. Royal Society Ser. A 229 281. 被引量:1
  • 2Gazis D C, Herman R and Rothery R W 1961 Oper. Res. 9 545. 被引量:1
  • 3Kuznetsov A V and Avramenko A A 2009 Math. Bio- sciences 218 142. 被引量:1
  • 4Qian Y S, Shi P J, Zeng Q, Ma C X, Lin F, Sun P and Yin X T 2009 Chin. Phys. B 18 4037. 被引量:1
  • 5Qian Y S, Shi P J, Zeng Q, Ma C X, Lin F, Sun P and Wang H L 2010 Chin. Phys. B 19 048201. 被引量:1
  • 6Belitsky V, Maric N and Schtitz G M 2007 J. Phys. A 40 11221. 被引量:1
  • 7Foulaadvand M E and Belbasi S 2007 J. Phys. A 40 8289. 被引量:1
  • 8Nagel K and Schreckenberg M 1992 J. Physique I (France) 2 2221. 被引量:1
  • 9Fukui M and Ishibashi Y 1996 J. Phys. Soc. Jpn. 65 1868. 被引量:1
  • 10Hu S X, Gao K, Wang B H and Lu Y F 2008 Chin. Phys. B 17 1863. 被引量:1

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