路网车流动态OD在线快速估计是利用道路交通供需关系实现主动交通管理的关键支撑。面向大规模城市路网，现有理论与方法存在动态交通分配模型难以精确描述真实路网车辆出行路径选择规律、OD估计模型调优求解费时费力、以OD为表征的路网交通需求时空演变规律探究不够深入等不足。以多源新兴身份车辆位置感知数据及传统道路断面检测流量数据为基础，本研究利用以深度学习为代表的人工智能技术，研究大规模城市路网车流动态OD在线快速估计方法，分析揭示以车流OD为表征的路网交通需求时空演变规律，形成了身份车辆轨迹精确重构、考虑增量更新机制的动态交通分配模型构建、基于启发式遗传搜索寻优的车流动态OD在线快速估计求解、路网交通需求时空交互特性及不确定性水平分析等关键技术方法。项目研究是利用新兴人工智能技术解决传统交通难题的一项有益尝试，对新形势下人工智能技术与传统交通工程理论的快速融合，推动交通工程理论新发展具有积极作用。

Online and fast estimation of dynamic flow OD for highway networks is critical to active traffic management that uses the relationships between highway supplies and traffic demands. For large- scale urban road networks, existing theories and methods are still constrained in that the dynamic traffic assignment models are incapable to accurately describe the rules of vehicle route choices for real road networks, the optimization process of finding OD estimation model solutions is time-consuming and grueling, and that the spatiotemporal evolution rules of highway network traffic demands are still superficially explored. Based on the multiple and new identifiable vehicle location data and the traditionally volume measurements collected at road stations, this research studies the online and fast estimation methods of dynamic flow OD and explores the spatiotemporal evolution rules of traffic demands that are represented by flow OD, for large-scale urban road networks. As results, key technologies and methods including accurate trajectories reconstruction for identifiable vehicles, dynamic traffic assignment model construction that considers the incremental updating mechanism, dynamic flow OD estimation based on heuristic genetic search optimization algorithm, and the analysis of spatiotemporal interaction characteristics and uncertainty levels of traffic demands for road networks, are formed. The study is a valuable attempt that uses new AI technologies in solving traditionally difficult traffic problems. As well, the study will have positive effects in fast fusing the new AI technologies and traditional traffic engineering theories and herein promoting the new development of traffic engineering theories under new circumstances.