北京航空航天大学学报 ›› 2008, Vol. 34 ›› Issue (02): 234-238.

• 论文 • 上一篇    下一篇

基于元胞传输模型的实时交通信息设计

尚华艳1, 黄海军1, 高自友2   

  1. 1. 北京航空航天大学 经济管理学院, 北京 100083;
    2. 北京交通大学 交通运输学院, 北京 100044
  • 收稿日期:2007-03-16 出版日期:2008-02-29 发布日期:2010-09-17
  • 作者简介:尚华艳(1978-),女,湖北当阳人,博士生,shanghuayan@126.com.
  • 基金资助:

    国家973基础研究计划资助项目(2006CB705503); 国家自然科学基金资助项目(70521001);北京航空航天大学博士研究生创新基金资助项目(430261); 北京市科委博士生论文资助专项资金资助项目

Design real-time traffic information by cell transmission model

Shang Huayan1, Huang Haijun1, Gao Ziyou2   

  1. 1. School of Economics and Management, Beijing University of Aeronautics and Astronautics, Beijing 100083, China;
    2. School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
  • Received:2007-03-16 Online:2008-02-29 Published:2010-09-17

摘要: 随着智能交通技术的发展,可变信息标志(VMS,Variable Message Signs)被广泛应用于动态交通管理中.元胞传输模型(CTM,Cell Transmission Model)可以很好地模拟交通流激波、排队形成与消散等交通流动力学特性.提出一种新的基于CTM的路径行驶时间计算方法,并使用LOGIT原则计算路径选择概率,针对重复性拥挤和非重复性拥挤2种交通状况,分析了VMS对交通流的影响.数值模拟结果表明,理论模型能够合理反映现实的交通状况:发生重复性拥挤时,VMS能够合理疏导人们的出行,减缓交通拥挤;而发生非重复性拥挤时,VMS对高需求和低需求情形下的交通流影响相似,但在高需求情形下,路径选择对模型参数的敏感区很小.

Abstract: The variable message signs (VMS) have been widely used in guiding and managing the dynamic traffic with development of intelligent transportation technologies.Such traffic dynamics as shock waves and jam can be well reproduced by cell transmission model (CTM). A new method to estimate the route travel times based on the CTM was presented and the LOGIT rule to govern the route choice was adopted. The impacts of VMS on recurrent and non-recurrent congestion were numerically investigated using the formulae proposed. The real-life traffic conditions could well be reproduced as simulation results shown. Travelers- decision making could be positively influenced by the VMS so as to alleviate the grave degree of recurrent congestion. In the case of non-recurrent congestion, the VMS induced similar effects no matter high and low the traffic demand was. However, the resultant flow pattern was sensitive within a small range of the model parameter when the demand was high.

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