基于加权流量介数中心性的路网脆弱性分析——以无锡市为例

针对当前路网脆弱性研究中缺乏对真实交通状况考量的问题，在复杂网络理论的基础上，结合交通流量信息，提出了基于加权流量介数中心性的路网脆弱性分析方法。首先计算路网拓扑抽象图中各节点的最短路径介数中心性，然后使用流量数据对相应区域最短路径介数中心性加权，综合得到最终的脆弱性指标结果。以无锡市为例，对其实际交通路网脆弱性进行了计算，结果表明，该方法能综合反映静态全局路网结构与动态局部通行信息和现实交通情景下的路网脆弱性。

Assessing vulnerability of road networks based on traffic flow betweenness centrality: A case study in Wuxi

Road networks are vulnerable to many events. Thus protecting the essential part of roads is important in rescue and emergency cases. Traffic Flow Betweenness Centrality (TFBC) was introduced to the study of network vulnerability considering both static network structure and dynamic demands of traffic. Taking Wuxi city as a case, using road information from the open street map and taxi trajectory data, the vulnerability of city road network was estimated. Vulnerability maps and statistics are presented to compare with the results by other vulnerability compute methods for validation and verification. The results show that:（1) Both TFBC and static network structure analysis gave high scores of vulnerability to streets that are near metropolitan areas. Those streets are the main vessels connecting different parts of the city. （2) TFBC identified important city center hubs, such as train stations, hospitals, flyovers as vulnerable. However, the vulnerability of these essential parts was underestimated by the static network structure analysis. （3) Nodes were assigned to low vulnerability in rural areas by TFBC. In contrast, static network structure analysis assigned high value to roads in villages, especially those which were connected to the metropolitan area. It seemed not reasonable since few people are using those road. The method of TFBC in road network research can effectively reveal the level of vulnerability by considering both static network structure and dynamic demands of traffic, which is of great significance to understanding the runtime pattern of road networks. The data source of TFBC is not limited to taxi trajectories, but can also use trajectory data generated by cell phones, bicycles and other motor vehicles. With more trajectory data provided in future studies, the results calculated by TFBC will be more accurate. Our research results are of great value to the related policy-making in urban transportation planning and management.