Abstract
The traditional full-scan method is commonly used for identifying critical links in road networks. This method simulates each link to be closed iteratively and measures its impact on the efficiency of the whole network. It can accurately identify critical links. However, in this method, traffic assignments are conducted under all scenarios of link disruption, making this process prohibitively time-consuming for large-scale road networks. This paper proposes an approach considering the traffic flow betweenness index (TFBI) to identify critical links, which can significantly reduce the computational burden compared with the traditional full-scan method. The TFBI consists of two parts: traffic flow betweenness and endpoint origin–destination (OD) demand (rerouted travel demand). There is a weight coefficient between these two parts. Traffic flow betweenness is established by considering the shortest travel-time path betweenness, link traffic flow and total OD demand. The proposed approach consists of the following main steps. First, a sample road network is selected to calibrate the weight coefficient between traffic flow betweenness and endpoint OD demand in the TFBI using the network robustness index. This index calculates changes in the whole-system travel time due to each link’s closure under the traditional full-scan method. Then, candidate critical links are pre-selected according to the TFBI value of each link. Finally, a given number of real critical links are identified from the candidate critical links using the traditional full-scan method. The applicability and computational efficiency of the TFBI-based approach are demonstrated for the road network in Changchun, China.
Highlights
Urban road networks are the lifeblood of the development of cities and significantly affect the travel of residents and the logistics of production
This article proposes a novel approach based on the traffic flow betweenness index (TFBI) to identify critical links in large-scale networks
In the TFBI-based approach, candidate critical links are pre-selected according to their TFBI values, and the preferred number of critical links is determined from the candidate critical links using the traditional full-scan method
Summary
Urban road networks are the lifeblood of the development of cities and significantly affect the travel of residents and the logistics of production. Links are often interrupted by natural hazards, traffic accidents. The failure of certain critical links can significantly degrade a road network’s performance and can even trigger a cascading failure, paralysing the network. Critical links and the traffic flow betweenness index in the Supporting information S1 File. S1 File includes the Changchun road network structure. S1 File can be open by TransCAD, which is traffic software for traffic planning. The actual survey data, such as link traffic flow and free-flow travel time, were collected through actual investigation by Changchun Municipal Engineering Design and Research Institute
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