交通运输系统工程与信息

交通运输系统工程与信息 ›› 2020, Vol. 20 ›› Issue (4): 41-48.

• 智能交通系统与信息技术 • 上一篇    下一篇

城市干线短交织区元胞自动机多级换道决策模型

彭 博1a, 1b，王玉婷1b，谢济铭*2，张媛媛1b，唐 聚1b   

  1. 1. 重庆交通大学 a.山地城市交通系统与安全重庆市重点实验室，b.交通运输学院，重庆 400074；2.昆明理工大学 交通工程学院，昆明 650224
  • 收稿日期:2020-03-03 修回日期:2020-04-22 出版日期:2020-08-25 发布日期:2020-08-25
  • 作者简介:彭博(1986-)，男，四川南充人，副教授.
  • 基金资助:

    国家自然科学基金/ National Natural Science Foundation of China(61703064)；重庆市基础前沿与技术创新项目/Chongqing Research Program of Basic Research and Frontier Technology Innovation(cstc2018jscx-msybX0295)；山地城市交通系统安全实验室开放基金/ Scientific Research Project of Traffic System & Safety in Mountain Cities(2018TSSMC05).

Multi-stage Lane Changing Decision Model of Urban Trunk Road's Short Weaving Area Based on Cellular Automata

PENG Bo1a, 1b, WANG Yu-ting1b, XIE Ji-ming2, ZHANG Yuan-yuan1b, TANG Ju1b   

  1. 1a. Chongqing Key Lab of Traffic System & Safety in Mountain Cities, 1b. College of Traffic and Transportation, Chongqing Jiaotong University,Chongqing 400074, China; 2. School of Traffic Engineering, Kunming University of Scienceand Technology, Kunming 650224, China
  • Received:2020-03-03 Revised:2020-04-22 Online:2020-08-25 Published:2020-08-25

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摘要：

为探索城市干线短交织区交通运行特性，基于高精度车辆轨迹数据，提出细化元胞尺寸与步长的交织区元胞自动机多级换道决策模型.划分上下游、交织影响区等多个分区，独立设置变量与规则进行建模；考虑车辆换道速度差、间距及换道安全风险，建立上下游换道模型，交织影响区多级换道决策模型；对未分区换道模型(I)，分区STCA换道模型(II)，分区多路合流换道模型(III)，本文模型(IV)进行仿真验证.与实测数据相比，本文模型平均车道流量误差仅为 1.64%. 模型 I~IV 在交织影响区的平均速度误差分别为 98.35%、23.77%、16.46%、7.45%，换道次数误差分别为33.34%、97.75%、62.97%、11.85%.结果表明，本文模型能有效模拟短交织区复杂的换道行为及交通流特性.

关键词: 智能交通, 换道决策, 分区建模, 短交织区, 元胞自动机

Abstract:

In order to explore traffic characteristics of urban trunk road's short weaving areas, a multi-stage lane changing decision model with refined cellular automata size and step time for weaving areas was proposed, based on high resolution vehicle trajectory data. Firstly, zone segmentation was conducted, including upstream, downstream, weaving influence area, and so on, which was modeled by cellular automata with independent parameters and rules. Then, a lane changing model for upstream and downstream, and a multi-stage lane changing decision model of weaving influence area were established considering speed difference, vehicle gaps, and safety risks when changing lanes. At last, simulation and validation were carried out for the lane changing model without zoning (I), the STCA lane changing model with zoning (II), the multi- lane merging lane changing model with zoning (III), and the proposed model (IV). In comparison with field data, average lane traffic volume error of our model is 1.64% , average speed errors of model I ~ model IV are 98.35% , 23.77% , 16.46% , and 7.45% , respectively, for weaving influence area, and mean errors of lane changing times of model I ~ model IV are 33.34% , 97.75% , 62.97% , and 11.85% respectively. Therefore, the proposed model can effectively simulate complex lane changing behaviors and traffic characteristics of short weaving areas.

Key words: intelligent transportation, lane changing decision, zoning modeling, short weaving area, cellular automata

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