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Identifying critical road segments and measuring system-wide robustness in transportation networks with isolating links: A link-based capacity-reduction approach
Authors:J.L. Sullivan  D.C. Novak  L. Aultman-Hall  D.M. Scott
Affiliation:1. Transportation Research Center, University of Vermont, 210 Colchester Avenue, Farrell Hall, Burlington, VT 05405, United States;2. School of Business Administration, University of Vermont, 55 Colchester Avenue, 310 Kalkin Hall, Burlington, VT 05405, United States;3. TransLAB (Transportation Research Lab), School of Geography and Earth Sciences, McMaster University, Hamilton, Ontario, Canada L8S 4K1
Abstract:A wide range of relatively short-term disruptive events such as partial flooding, visibility reductions, traction hazards due to weather, and pavement deterioration occur on transportation networks on a daily basis. Despite being relatively minor when compared to catastrophes, these events still have profound impacts on traffic flow. To date there has been very little distinction drawn between different types of network-disruption studies and how the methodological approaches used in those studies differ depending on the specific research objectives and on the disruption scenarios being modeled.In this paper, we advance a methodological approach that employs different link-based capacity-disruption values for identifying and ranking the most critical links and quantifying network robustness in a transportation network. We demonstrate how an ideal capacity-disruption range can be objectively determined for a particular network and introduce a scalable system-wide performance measure, called the Network Trip Robustness (NTR) that can be used to directly compare networks of different sizes, topologies, and connectivity levels.Our approach yields results that are independent of the degree of connectivity and can be used to evaluate robustness on networks with isolating links. We show that system-wide travel-times and the rank-ordering of the most critical links in a network can vary dramatically based on both the capacity-disruption level and on the overall connectivity of the network. We further show that the relationships between network robustness, the capacity-disruption level used for modeling, and network connectivity are non-linear and not necessarily intuitive. We discuss our findings with respect to Braess’ Paradox.
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