New insights into traffic dynamics: a weighted probabilistic cellular automaton model
Li Xing-Li(李兴莉)a), Kuang Hua(邝华)a)b), Song Tao(宋涛)a), Dai Shi-Qiang(戴世强)a)†, and Li Zhi-Peng(李志鹏)c)
aShanghai Institute of Applied Mathematics and Mechanics, Shanghai University, Shanghai 200072, China; bCollege of Physics and Electronic Engineering, Guangxi Normal University, Guilin 541004, China; cSchool of Electronics and Information Engineering, Tongji University, Shanghai 200092, China
Abstract From the macroscopic viewpoint for describing the acceleration behaviour of drivers, a weighted probabilistic cellular automaton model (the WP model, for short) is proposed by introducing a kind of random acceleration probabilistic distribution function. The fundamental diagrams, the spatiotemporal patterns, are analysed in detail. It is shown that the presented model leads to the results consistent with the empirical data rather well, nonlinear flow--density relationship existing in lower density regions, and a new kind of traffic phenomenon called neo-synchronized flow. Furthermore, we give the criterion for distinguishing the high-speed and low-speed neo-synchronized flows and clarify the mechanism of this kind of traffic phenomenon. In addition, the result that the time evolution of distribution of headways is displayed as a normal distribution further validates the reasonability of the neo-synchronized flow. These findings suggest that the diversity and the randomicity of drivers and vehicles have indeed a remarkable effect on traffic dynamics.
Received: 06 November 2007
Revised: 28 November 2007
Accepted manuscript online:
(Granular models of complex systems; traffic flow)
Fund: Project supported by the National
Basic Research Program of China (Grant No 2006CB705500), the
National Natural Science Foundation of China (Grant Nos 10532060 and
10562001), the Special Research Fund for the Doctoral Program in
Higher Education of China (Grant No SRFDP 20040280014) and the
Shanghai Leading Academic Discipline Project of China (Grant No
Y0103).
Cite this article:
Li Xing-Li(李兴莉), Kuang Hua(邝华), Song Tao(宋涛), Dai Shi-Qiang(戴世强), and Li Zhi-Peng(李志鹏) New insights into traffic dynamics: a weighted probabilistic cellular automaton model 2008 Chin. Phys. B 17 2366
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