College of Transportation, Jilin University, Changchun, Jilin, China.
Rail Transit Institute, Jilin Communications Polytechnic, Changchun, Jilin, China.
PLoS One. 2020 Apr 10;15(4):e0227474. doi: 10.1371/journal.pone.0227474. eCollection 2020.
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.
传统的全扫描方法常用于识别路网中的关键链路。该方法模拟每个链路依次关闭，并测量其对整个网络效率的影响。它可以准确识别关键链路。然而，在该方法中，在链路中断的所有情况下都进行交通分配，这使得该过程在大规模路网中非常耗时。本文提出了一种考虑交通流量介数（TFBI）的方法来识别关键链路，与传统的全扫描方法相比，可以显著降低计算负担。TFBI 由两部分组成：交通流量介数和端点起讫点（OD）需求（重路由旅行需求）。这两部分之间存在权重系数。交通流量介数是通过考虑最短旅行时间介数、链路交通流量和总 OD 需求来建立的。该方法包括以下主要步骤。首先，选择一个样本路网，使用网络稳健性指数校准 TFBI 中交通流量介数和端点 OD 需求之间的权重系数。该指数计算了在传统的全扫描方法下，由于每条链路的关闭，整个系统旅行时间的变化。然后，根据每条链路的 TFBI 值预选候选关键链路。最后，使用传统的全扫描方法从候选关键链路中识别出给定数量的真实关键链路。基于 TFBI 的方法的适用性和计算效率在中国长春的路网中得到了验证。
