基于活动的电动汽车使用者出行行为演化机理及建模

This research will be focused on the users of Electric Vehicles (EVs). First, the factors influencing the EV trip behaviour will be identified and the trip behaviour will be characterized using multi-sources data, including household trip survey data, Stated Preference (SP) survey data in time-series style and dynamic Global Positioning Satellite (GPS) data of EVs. Then, the utility maximization theory will be employed to develop a utility function for EV users to make decisions on performing activities and trip, based on which an activity-based EV trip demand generation model will be proposed. Next, the model will be applied to reveal the mechanisms of individual EV trip behaviour with the consideration of several driving factors, including the EV market penetration rate, the layout of EV charging infrastructures, traffic information guidance, and relevant polices. Primarily based on the SP survey data in time-series style, the evolutionary mechanism of individual EV trip behaviour will also be revealed. According to the evolutionary mechanism, an activity-based route choice model for EV drivers will be proposed with the application of agent-based model, the utility maximization theory, the prospect theory, and the path searching algorithm. At last, a large-scale simulation platform that incorporates the activity-based model, route choice model, energy consumption factor and emission factor will be developed for Beijing using the MATSim-based software that was developed by our research group. With the platform, the impact of using EVs in Beijing on the traffic operation will be assessed and the energy saving and emission reduction of using EVs will be quantified.

本项目以电动汽车使用者为研究对象，利用居民出行调查数据，并进行时间序列式SP调查，结合电动汽车运行GPS动态监测数据，分析影响出行的关键因素和出行行为特征，应用效用最大化理论，建立电动汽车使用者活动和出行决策的效用函数，构建基于活动的电动汽车出行需求模型；在此基础上，以电动汽车市场占有率、充电设施布局、交通信息诱导、相关政策等外界因素为驱动因子，刻画电动汽车使用者个体出行行为机理；利用时间序列式SP调查数据分析电动汽车使用者的出行行为演化机理，利用多智能体仿真技术、效用最大化理论、前景理论、路径搜索算法等构建基于活动的电动汽车使用者交通出行路径选择模型；以项目组基于MATSim仿真工具开发的系统为依托，融合基于活动的出行需求模型和路径选择模型，结合能耗和尾气排放因子，搭建适用于北京市规模的仿真平台，同时进行仿真模拟，给出电动汽车混入对交通运行的影响及节能减排效果。

本项目通过SP调查数据，对居民的电动车购买行为、个体停车和充电行为进行了研究。其次，以MATSim为仿真模拟平台，对基于活动的出行需求模型的结构及参数进行研究，融合了多种与电动汽车个体出行相关的多智能微观仿真模型，并融入行驶过程中的加油行为模块，以及目的地的停车行为模块，从而可以对个体出行过程进行精细化模拟。再次，以MATSim平台为基础，开发了基于GIS和多智能体的微观虚拟城市生成模块SelfSim，进而对北京市的小汽车个体出行行为进行了模拟，探究了电动汽车出行行为演化规律，并提出了基于Agent的常规车辆和电动车辆的交通设施布局模型，开发了基于Agent的RLC-REP联合模型。最后，对基于多智能体的城市微观仿真模型SelfSim进行扩展和升级，增加电动汽车购买和出行行为相关模块，得到新的模型版本SelfSim-EV，模拟了北京市2011-2020年期间电动汽车购买和出行行为的情况，并对电动汽车出行和充电带来的设施需求、电力需求以及环境变化进行了评估，进而将电动汽车补贴政策、电力价格和汽油价格作为外界驱动因子，利用SelfSim-EV研究这些外界因素对电动汽车购买以及出行行为的影响，并分析了电动汽车使用者出行行为演化对交通运行的影响和节能减排效果。