客流特征视角下的轨道交通网络特征及其脆弱性

为提高城市轨道交通网络脆弱性评估的客观性, 将乘客需求特性集成到网络脆弱性的计算中; 在城市轨道交通网络Space L空间下静态拓扑结构的基础上, 以客流为权重建立了轨道交通加权网络; 基于客流指标提出了车站连接强度和加权节点介数, 用于反映动态网络结构特征, 度量节点间相互作用强度; 针对城市轨道交通网络客流的时空特性, 结合网络客流需求特性, 基于出行消耗最大容限阈值, 构建了站点故障条件下的乘客有效路径子图和网络客流的OD损失率, 进而评估城市轨道交通网络的脆弱性; 以西安城市轨道交通网络为例, 从网络客流视角分析了城市轨道交通网络特征及其脆弱性。研究结果表明: 西安市轨道交通网络具有小世界网络特性, 平均路径长度为10.7, 其中小寨站和北大街站为网络关键节点, 其车站连接强度分别为166 795、149 059, 加权节点介数分别为0.365、0.369, 这两个站点的中断对西安市轨道交通网络效率的影响分别为40.1%、39.4%;乘客出行容限阈值极大地影响着网络中站点的重要性排序, 网络脆弱性随着乘客出行容限阈值的增大而逐渐降低; 脆弱性与介数的相关性强于脆弱性与度和强度的相关性, 随着出行容限阈值的增大, 加权介数与其脆弱性的关联性逐渐降低。可见, 提出的计算指标和方法突出了客流特征与乘客需求对轨道交通网络脆弱性的影响, 能够很好地体现轨道交通网络的功能特性。 

Characteristics and vulnerability of rail transit network besed on perspective of passenger flow characteristics

In order to improve the objectivity of vulnerability assessment of urban rail transit network, passenger demand characteristics were integrated into the calculation of network vulnerability. Based on the static topological structure of urban rail transit network established by using the Space L method, the weighted network of rail transit was established with passenger flow as the weight. Based on the passenger flow index, the station connection strength and weighted node betweenness were proposed to reflect the dynamic network structure characteristics and measure the interaction strength between nodes. Aiming at the spatial-temporal characteristics of passenger flow in urban rail transit network, combined with the demand characteristics of network passenger flow, the passenger effective path subgraph and OD loss rate of network passenger flow under the condition of station failure were defined by using the maximum travel consumption tolerance threshold to evaluate the vulnerability of urban rail transit network. Taking Xi'an urban rail transit network as an example, the features and vulnerability of urban rail transit network were interpreted from the perspective of passenger flow characteristics. Research result shows that the current rail transit network in Xi'an has the characteristic of small world network and its average path length is 10.7. Xiaozhai Station and Beidajie Station are the key nodes of the network, their connection strengths are 166 795 and 149 059, respectively, and their weighted node betweennesses are 0.365 and 0.369, respectively. The interruption of the two stations will result in 40.1% and 39.4% reduction in network efficiency. The passenger travel tolerance threshold greatly affects the importance ranking of the stations in the network. With the increase of passenger travel tolerance threshold, the network vulnerability gradually decreases. The correlation between vulnerability and betweenness is stronger than those with degree and intensity. With the increase of travel tolerance threshold, the correlation between weighted betweenness and vulnerability gradually decreases. Therefore, the calculation indicators and methods proposed in this paper highlight the impact of passenger flow characteristics and passenger demand on the vulnerability of rail transit network, which can well reflect the functional characteristics of rail transit network.