Monitoring of operational resilience on urban road network: A Shaoxing case study

Abstract

Urban road networks (URNs), which are the critical infrastructure of a city, are fragile when faced with external disruptions. Efficient and accurate analyses of URN resilience of URNs could provide a new perspective for enhancing their ability to withstand, adapt, and recover from disruptive events. This study focused on the resilience evaluation and prediction of URN. A time-varying belief Markov-based resilience model was proposed to analyze the Operational Resilience (OR), which integrates link usability and driving efficiency. The OR is then converted to a normalized scale (COR), which is easier for decision-makers to understand. Finally, a case study was conducted to validate the proposed model. The results showed that demand, link capacity, disaster intensity, and road network structure are significant factors affecting the OR of a URN. Within the OR threshold, the recovery time is generally half the response time and is more stable among different links and precipitation intensities. It has been proven to have satisfactory performance in the estimation and prediction of resilience, which can capture the long-term OR of URN and identify key links and regions that require more attention. This approach could assist decision-makers in developing effective measures for disruptive events.