城市轨道交通网络运营中断场景下的客流疏散研究

城市轨道交通网络运营中断是城市应急管理的重点对象，具有一定的不可预见性。一旦应急组织方式不合理，不仅会造成乘客的大面积延误，引发本区域及相邻车站的运营秩序混乱，严重者甚至会对城市秩序和公共安全造成难以估量的影响。因此，本项目对城市轨道运营网络中断场景下的客流疏散优化问题展开深入研究，系统地考虑轨道交通中断场景的差异性、网络结构复杂性、乘客需求多样性、接驳资源有限性等各种因素。从量化不同运营中断场景的时空特征出发，探索乘客面临不同运营中断场景的出行行为特征；结合历史客流数据，利用大数据挖掘技术，对短时客流进行精细化预测；面对不同运营中断场景，从时刻表调整层面引导客流在轨道交通网络上的时空分布，从接驳资源的实时调度层面响应客流的外溢接驳需求，两者相互协同，有效疏解客流拥塞区域。通过本项目的研究，旨在为城市轨道交通面临运营中断情况下的应急管理提供理论和方法支撑。

The operation disruption in urban rail transit network has the unpredictable characteristic. Once it occurs, large delay of passengers will be caused, and the operations in the disruption region and adjacent stations will be confused, and even public safety is threaten if the emergency organization is unreasonable. In this project, we will discuss deeply the optimization for the evacuation of passengers flow under the operation disruption scenarios in urban rail transit networks. Some factors are considered systematically, such as the difference of disruption scenarios, the complexity of networks structure, the diversity of passengers demand and the limited transfer resources availability. Firstly, the time-space characters in the different failure scenarios are quantified. Furthermore, we explore the travel behavior of passengers when facing different operation failure scenarios. Combined with history data, we adopt the big-data mining to predict passenger OD in the future. For the different operation disruption scenarios, the time-space distribution of passenger flows is adjusted from the level of train real-time reschedule, and the passenger demands for the bridge are responded through the real-time schedule of bridge resources. Through the coordination between train timetable reschedule and the bridge resource schedule, passenger flows in the congested regions can be evacuated effectively. This project hopes to provide the support of theories and methods for the further improvement of efficient and scientific emergency management under the operation disruptions in urban rail transit network.