本项目围绕城市公共交通电动化的关键规划与运营问题展开研究，致力于提高电动化交通系统运营效率与服务水平。首先，研究通过数据驱动的方法，对电动公交车系统、电动出租车与网约车系统的规划进行优化。其次，考虑交通系统随机性，研究电动公交车系统、电动出租车与网约车系统的动态运营策略优化。最后，研究建立数据化综合仿真平台，对关键理论进行验证评估。项目的主要特色包括：①项目采用“数据-知识-仿真-优化”的研究思路，运用海量交通信息数据建立仿真模型揭示城市电动交通系统运行机理，利用仿真优化方法优化电动化交通系统规划。②考虑随机交通状态以及出行需求不确定，项目采用增强学习和近似动态规划技术，依靠系统运营数据的实时采集以及信息在车辆与运营中心间的高效传输，实现对运营策略的动态优化以及优化效果的在线提升；③在规划与运营策略优化当中，项目考虑了多种决策变量的联合优化，还对多种模式的电动交通的运营策略进行了协同优化。

This project focuses on the planning and operations of the electrified urban public transportation and aims at improving both the operations efficiency and service level of the electrified transportation system. First, the project plans to apply the data-driven methods for optimizing the planning of urban electrified bus system and electrified taxi/ride-sourcing service system (UEBTS). Second, considering the stochasticity of urban traffic, the project investigates how to optimize the dynamic operations of UEBTS. Finally, the project builds the data-centric simulation framework to evaluate the proposed key methods. The innovations of this project lie in: 1) It follows the framework of data-knowledge-simulation-optimization, applies the big transportation data to build the simulation model to reveal the operations characteristics of UEBTS, and adopts the simulation-based optimization to optimize the planning of UEBTS; 2) Considering the stochastic travel times and customer demands and relying on the constantly updated system-operations information and the efficient communication between transit agencies and bus drivers, the project applies the reinforcement learning and approximate dynamic programming for dynamically optimizing the operations of UEBTS and continuously improving the optimization effects; 3) During the optimization of planning and operations strategies, the project considers the joint optimization of multiple decision variables, and further co-optimizes the operations of multi-modal electrified transportation system.