交通运输系统工程与信息 ›› 2010, Vol. 10 ›› Issue (3): 35-41 .

• 智能交通系统与信息技术 • 上一篇    下一篇

考虑控制策略的公交运输系统元胞自动机模型

丁建勋;黄海军*   

  1. 北京航空航天大学 经济管理学院,北京 100191
  • 收稿日期:2009-08-13 修回日期:2009-11-27 出版日期:2010-06-25 发布日期:2010-06-25
  • 通讯作者: 黄海军
  • 作者简介:丁建勋(1981-),男,安徽淮北人,博士生
  • 基金资助:

    国家“973”基础研究计划(2006CB705503);国家自然科学基金(70521001)

A Cellular Automaton Model of Public Transport System Considering Control Strategy

DING Jian-xun;HUANG Hai-jun   

  1. School of Economics and Management, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
  • Received:2009-08-13 Revised:2009-11-27 Online:2010-06-25 Published:2010-06-25
  • Contact: HUANG Hai-jun

摘要: 针对公共交通系统,受蚁群移动轨迹行为启发,视停靠站处等待乘客信息为一种外在的“信息素”(pheromone),即可实现相继公交车的信息传递,基于此本文提出一个新的可变跃迁概率元胞自动机(CA)模型. 模型中假设公交车辆并非唯一追求以最大速度行驶,依据“信息素”引入控制策略动态调整公交车前行速度,实现了公交线路上车辆之间的相互合作以及协调行驶行为. 数值模拟实验表明,模型可再现公交运行中的“集簇现象”,新的控制策略引入后模型在很大程度上改善了车辆在公交线路上的时空分布,疏散了公交车辆集簇行驶的状况,进而降低了平均等待乘客数. 结果表明,新的控制策略可以显著提升公交线路系统的运行效率,具有较强的现实指导意义.

关键词: 城市交通, 公交运输系统, 元胞自动机模型, 控制策略, 跃迁概率, 集簇现象

Abstract: Concerning the public transport system, inspired by the organization of traffic flow on ant trails, a new cellular automaton model of public traffic is proposed in which the number of waiting passengers in the bus stop is regarded as an external “pheromone”. In the model, the buses would cooperate with other buses while driving and adjust their speeds based on the difference value between the estimated headway from the waiting passenger’s information and the desired headway. Numerical simulation results show that the proposed model can reproduce clustering of the buses along the route. By incorporating a new control strategy, the model helps in reducing the undesirable tendency of clustering by dispersing the buses uniformly along the route, and decreasing the average number of waiting passengers in the public transport system. The system performance is then improved significantly with the control strategy, which has the extensive applicability and practical significance.

Key words: urban traffic, public transport system, cellular automaton model, control strategy, hopping probability, cluster phenomena

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