Similarly, Berdica described road network vulnerability as “susceptibility to incidents that can result in considerable reductions in road network serviceability”. defined road network reliability as a road network’s ability to handle a recurrent variation. Reliability, vulnerability, and criticality (critical components) are related concepts, but unified definitions of these concepts have not been universally accepted. Hence, identifying and enhancing critical links can avoid or mitigate the influence of failures on the network when chance events or intentional attacks occur. The failure of certain critical links can significantly degrade a road network’s performance and can even trigger a cascading failure, paralysing the network for some time. However, links are often interrupted by natural hazards, traffic accidents. Urban road networks are the lifeblood of the development of cities and significantly affect the travel of residents and the logistics of production. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. 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.įunding: This work is supported by Jilin Scientific and Technological Development Program under grant number 20190303124SF.
TRANSCAD OD SOFTWARE
S1 File can be open by TransCAD, which is traffic software for traffic planning. S1 File also includes the Changchun road network structure. The basic data used in our study are in the Supporting information S1 File. The authors extracted the basic data used in our study, such as link properties, link capacity, link traffic flow, and link free-flow travel time, from the primary data. The Institute is a partner of the project (Jilin Province Science and Technology Development Plan Project under Grant 20190303124SF) supporting our study. The primary data are obtained from Changchun traffic big data platform of Changchun Municipal Engineering Design and Research Institute. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All relevant data are within the paper and its Supporting Information files. Received: SeptemAccepted: DecemPublished: April 10, 2020Ĭopyright: © 2020 Li et al. PLoS ONE 15(4):Įditor: Yanyong Guo, University of British Columbia, CANADA The applicability and computational efficiency of the TFBI-based approach are demonstrated for the road network in Changchun, China.Ĭitation: Li F, Jia H, Luo Q, Li Y, Yang L (2020) Identification of critical links in a large-scale road network considering the traffic flow betweenness index. Finally, a given number of real critical links are identified from the candidate critical links using the traditional full-scan method. Then, candidate critical links are pre-selected according to the TFBI value of each link. This index calculates changes in the whole-system travel time due to each link’s closure under the traditional full-scan method. 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.
The proposed approach consists of the following main steps. Traffic flow betweenness is established by considering the shortest travel-time path betweenness, link traffic flow and total OD demand.
There is a weight coefficient between these two parts. The TFBI consists of two parts: traffic flow betweenness and endpoint origin–destination (OD) demand (rerouted travel demand). 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. 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. It can accurately identify critical links. This method simulates each link to be closed iteratively and measures its impact on the efficiency of the whole network. The traditional full-scan method is commonly used for identifying critical links in road networks.
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