基于动态客流的城市轨道交通关键站点识别

A novel method to identify influential stations based on dynamic passenger flows

定量评估城市轨道交通站点的重要性有助于优化城市轨道交通网络,提升针对突发事件的应急管理能力.现有工作常根据轨道拓扑结构或静态客流的分布来识别关键站点,然而,由于居民日常出行行为展现时空变化特征,它们对关键站点的识别也有重要影响.为此,本文提出一种结合轨道网络拓扑结构和动态客流的拓扑-客流中心性指标来动态识别轨道交通关键站点.首先,将轨道交通网络拓扑结构抽象为节点负载网络,利用节点负载刻画客流时变特征.其次,利用级联失效模型对比拓扑-客流中心性指标与其他中心性指标对网络平均效率、极大连通系数和损失客流的影响.大量实验表明所提指标能有效识别轨道交通网络中的关键站点.同时,关键站点会随客流演变展现出动态变化特征,特别是当客流量波动剧烈时最为显著.

Quantitative evaluation of station importance in subway networks can help optimize the urban rail transit networks and enhance the management capability for emergencies.Several existing studies have analyzed the important stations based on the rail structure or the static distribution of passenger flows.However,the spatiotemporal characteristics of residents in daily travel also play a crucial role when evaluating the station’s importance.Therefore,this paper proposes a novel evaluation method named topology-flow centrality for identifying the important stations by combining the topology of the subway networks and dynamic passenger flows.To begin with,the topology of a rail transit network is abstracted as a node load network,and the load of nodes is used to describe the time-varying characteristics of passenger flows.Then,according to cascading failure,this paper compares the topology-flow centrality and other centrality criteria on the impact of the average network efficiency and the lost passenger flow.Experimental results demonstrate that the proposed criterion can effectively identify the important stations in subway networks.Moreover,the importance of stations displays dynamism with the evolution of passenger flows,especially when the passenger flows fluctuate sharply.