Assessing transportation network redundancy by integrating route diversity and spare capacity

Abstract

AbstractIn this paper, we develop a new transportation network redundancy measure by integrating the route diversity and spare capacity dimensions, which have been demonstrated as two complementary dimensions of redundancy and assessed separately in the literature. Instead of modelling redundancy as two separate measures with significantly different units and scales, an integrative measure is beneficial for comprehensive comparison and selection of redundancy enhancement schemes. Specifically, we represent the transportation network by adding a virtual link directly connecting each origin-destination (O-D) pair. The virtual link choice probability is affected by both the route diversity of the O-D pair and the spare capacity of these routes, which is used in the proposed measure. Numerical examples in the realistic Winnipeg network are conducted. The effectiveness of the proposed measure is further verified by applying it to identify the critical links in the Shanghai Expressway Network, where a severe traffic accident happened in 2016.KEYWORDS: Redundancyroute diversityspare capacitycritical link identification Disclosure statementNo potential conflict of interest was reported by the authors.Author contributionsZijian Wang: methodology, investigation, and writing – original draft preparation. Xiangdong Xu: conceptualisation, methodology, supervision, and writing – reviewing and editing. Yuchuan Du: conceptualisation and writing – reviewing and editing.Notes1 The free-flow travel time of a link is the travel time of the link when its volume is equal to 0, and the minimum free-flow travel time of an O-D pair is the minimum travel time of the O-D pair when the volume of all links in the network is equal to 0.2 In Section 3.1, a route is represented by the links traversed by it.Additional informationFundingThe work described in this paper was sponsored by the National Key Research and Development Program of China (2021YFB1600100), the National Natural Science Foundation of China (71971159), and Fundamental Research Funds for the Central Universities of China. These supports are gratefully acknowledged.