城市干线短交织区元胞自动机多级换道决策模型

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.

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

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.