大客流下城市轨道交通站点韧性评估及划分

Resilience assessment and partition of an urban rail transit station under large passenger flow

为量化并提升城市轨道交通站点在大客流下的运营服务水平,以韧性为指标评估并划分了站点应对大客流的能力。首先,通过分析站点的3类乘客出行流线,考虑乘客在站点出行的便利性,提出了基于乘客在站出行时间的站点运营服务水平评价指标;接着,结合韧性三角理论,构建基于站点剩余运营服务水平的韧性指标评估,并运用模糊聚类分析法实现了站点韧性评估结果的划分;最后,评估了成都地铁各站点在早高峰大客流下的韧性。结果表明:早高峰期间站点的运营服务水平随时间呈3类变化趋势,分别对应于3类客流量分布;站点韧性与客流量及平均延误次数有关,1号线的站点因其客流量最大而拥有最差的韧性;聚类中心数的增加将加剧各类站点的重叠程度,成都地铁站点的韧性处于较低水平,着重关注1、2号线站点的运营有利于提升系统应对大客流的能力。

This paper intends to assess and partition an Urban Rail Transit(URT)station’s resilience under large passenger flow.Passengers’travel time at a URT station is discussed based on the station’s passenger streamline and queuing model.Considering passengers’travel convenience at a URT station,a convenience-based indicator(CBI)is proposed to evaluate a URT station’s service level under large passenger flow.Based on the resilience triangle,a URT station’s resilience is assessed with the residual CBI under the large passenger flow.Finally,the URT stations’resilience under large passenger flow is partitioned using the fuzzy c-means clustering model.The model application of the Chengdu subway indicates that there are three category relation curves among stations’CBI and time,which corresponds to three category passenger trips distributions during the morning peak hours.The CBI effectively evaluates a station’s performance under large passenger flow,and it is consistent with a station’s operations under the large passenger flow.Stations on line 1 possess the worst resilience since they account for 24.20%of the total number of passenger trips.Line 10’s stations have the highest resilience since the number of passenger trips on line 10 only accounts for 1.72%of the total number of passenger trips.Because transfer passenger trips decrease passengers’travel convenience at a transfer station,transfer stations have a lower average resilience under large passenger flow than non-transfer stations.Chengdu subway station’s resilience is mainly affected by passenger trips and the number of delays at the station.The operators can enhance a station’s resilience by limiting the number of inbound passengers and guiding passengers to wait at the platforms uniformly.The increase of the number of clusters increases the degree of overlap between different clusters.Stations on the Chengdu subway system possess relatively poor resilience.For its operators,they need to focus on the operations of stations on lines 1 and 2 to improve their ability to respond to large passenger flow.