交通运输系统工程与信息

交通运输系统工程与信息 ›› 2019, Vol. 19 ›› Issue (4): 50-54.

• 智能交通系统与信息技术 • 上一篇    下一篇

多车道元胞自动机换道决策模型的冲突处理策略

邓建华*，冯焕焕，葛婷   

  1. 苏州科技大学土木工程学院, 江苏 苏州 215011
  • 收稿日期:2018-12-12 修回日期:2019-04-06 出版日期:2019-08-25 发布日期:2019-08-26
  • 作者简介:邓建华(1972-)，男，湖南永兴人，副教授.
  • 基金资助:

    国家自然科学基金/National Natural Science Foundation of China(51808370)；苏州科技大学基金/ Natural Science Foundation of Suzhou University of Science and Technology(341311108，XKQ201305).

Conflict Handling Strategies of Lane-changing Decision Model of Multi-lane Cellular Automata

DENG Jian-hua, FENG Huan-huan, GE Ting   

  1. College of Civil Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
  • Received:2018-12-12 Revised:2019-04-06 Online:2019-08-25 Published:2019-08-26

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摘要：

换道模型是多车道元胞自动机交通流模型的核心子模块之一，在分析现实中驾驶员执行换道时处理车辆冲突过程的基础上，依据其蕴含的不同换道驾驶行为特征把驾驶员采取的换道冲突策略划分为保守型、机敏型与激进型3 类，并通过进一步优化车辆状态更新算法，提出了换道冲突处理多策略，车辆状态更新次序随机的多车道换道模型.运行模型获得不同空间占有率条件下，驾驶员分别采取保守、机敏或激进策略时所产生的换道动机次数和换道成功次数.通过数据分析发现：在特定空间占有率区间，不同换道冲突处理策略将引起较显著换道动机概率差异与换道成功概率差异.

关键词: 智能交通, 多车道元胞自动机, 换道决策模型, 驾驶行为特征, 冲突处理策略

Abstract:

Lane-changing model is one of the core sub-models of multi-lane cellular automata traffic flow model. Based on the analysis of the process of dealing with vehicle conflict when drivers change lanes in reality, according to the different characteristics of lane- changing driving behavior, the conflict strategies adopted by drivers are divided into conservative, astute and radical ones. By further optimizing the vehicle status update algorithm, a multi-lane change model with multi-strategy and random order of vehicle status updating is proposed. Under different occupancy conditions, lane- changing motives, successful lane- changing times are generated by running the proposed multi- lane change model when drivers adopt conservative, astute or radical strategies. Through analysis, it is found that different lane- changing conflict handling strategies will lead to significant difference in lane- changing motivation probability and lane- changing success probability in specific space occupancy rates.

Key words: intelligent transportation, multilane cellular automaton, lane- changing decision model, driving behavior characteristics, conflict handling strategy

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