Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/147083
Title: Identification of critical links in a large-scale road network considering the traffic flow betweenness index
Authors: Li, Feiyan
Jia, Hongfei
Luo, Qingyu
Li, Yongxing
Yang, Lili
Keywords: Engineering::Civil engineering
Issue Date: 2020
Source: 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. PloS One, 15(4). https://dx.doi.org/10.1371/journal.pone.0227474
Journal: PloS One 
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.
URI: https://hdl.handle.net/10356/147083
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0227474
Rights: © 2020 The Author(s). 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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:CEE Journal Articles

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