基于网络客流传播的轨道交通关键站点识别
Critical Station Identification Based on Passenger Propagation in Urban Mass Transit Network

为了更为真实的反映城市轨道交通网络的实际运营情况，在复杂网络理论基础上，进一步考虑客流因素的影响，提高网络中关键站点识别的准确性，通过分析站区间断面客流来源，根据普通站和换乘站的客流运输功能特征，分别构建了客流传播模型，对历史刷卡数据配流统计标定模型参数，并结合复杂网络的度与介数提出了4个关键站点识别指标.以某市轨道交通网络为例，利用刷卡数据对某工作日早高峰时段进行了全网动态客流演示并展示关键车站.研究结果表明：关键线路为1号线与10号线，南站、西二旗、天通苑附近乘客滞留严重，客流负荷强度大的车站更易受到大客流的冲击；本文所构建的客流传播模型可动态显示全网各区间等级及滞留车站的变化，并能综合真实客流、线路运输能力以及线网结构三方面的指标识别关键站点，可更有效地为轨道交通网络安全管理提供参考.
：In order to reflect the real-world operation situation of an urban mass transit network, on the basis of complex network theory, the influence of passenger flow was considered to improve the recognition accuracy of the key stations in the network. By analyzing the sources of passenger flow in each section of the urban mass transit network, passenger propagation models were proposed according to the functional characteristics of passenger transport in ordinary stations and transfer stations. The model parameters were determined statistically by network flow assignment according to historical smart card data. Then, in combination with the degree and betweenness concepts in a complex network, four indexes were proposed to identify the critical stations. Taking the subway network of a certain city as an example, the dynamic passenger flow in the whole network was demonstrated and the critical stations were revealed by using smart card data on the morning peak hours of a certain workday. Research results show that the critical lines are Line 1 and Line 10. Passengers become stranded at the South Railway Station, Xi'erqi Station, and Tiantongyuan Station. The stations exposed to huge passenger flows are more vulnerable to the impacts of large passenger flow. The developed passenger propagation model can display the levels and variations in each section and station where passengers become stranded dynamically. The model can also identify the critical stations by considering the indexes of real passenger flow volume, transport capacity, and network structure. This will provide a theoretical reference for security management of urban mass transit with more efficiency