Interdependency analysis of junctions for congestion mitigation in Transportation Infrastructures

Abstract

The resilience of the Transportation road infrastructure network is of major importance, since failures such as prolonged road congestion in specific parts of the infrastructure often initiate major cascading effects that block transportation and/or disrupt services of other infrastructures over wide areas. Existing traffic flow analysis methods lack the ability to understand cascading effect of congestions and how to improve overall resilience in greater areas. Dependency risk graphs have been proposed as a tool for analyzing such cascading failures using infrastructure dependency chains. In this paper, we propose a risk-based interdependency analysis methodology capable to detect large-scale traffic congestions between interconnected junctions of the road network and provide mitigation solutions to increase traffic flow resilience. Dependency risk chains of junctions provide important information about which junctions are affected when other major junctions are congested in the road transportation network. Targeted mitigation mechanisms for traffic congestion can be proposed and the causes of bottlenecks can be analyzed to introduce road constructions or reparations with the best possible results in relieving traffic. We applied the proposed methodology on data collected by the UK government using cyber-physical traffic sensors over the course of 6 years. Our tool analyzed the UK major/A road transportation network, detected n-order junction dependencies and automatically proposed specific mitigation solutions to increase the overall resilience of the road infrastructure network. Simulation results indicate that detected mitigation options, if applied, can increase overall congestion resilience in wider areas of the network up to 12% by lowering likelihood of congestion.