Estimation of road network reliability on resiliency: An uncertain based model

Abstract

Past research shows that a better understanding of reliability and identification of ways to improve it can help a system's response to a disaster, leading to increased transportation system resilience. This study focuses on the quantification of improved reliability, which reduces the time of annealing and recovery post uncertain disruption. A reliability model is presented by using three performance functions that estimate the total travel time, flow, and consumer surplus. Network reliability is estimated by considering uncertainties in link-capacity and demand sensitivity with respect to travel time, following a disaster. Sensitivity and uncertainty analyses are conducted to identify the most crucial links in the transportation network, for which resistance should be increased to mitigate disaster risk. The simulation results show that the model provides accurate predictions of the system performance, and that a reliability model that accounts for uncertainty yields better results than a deterministic (no uncertainty) model. With higher accuracy models, planners are able to make informed decisions in disaster mitigation planning.