Percolation-Based Resilience Modeling and Active Intervention of Disrupted Urban Traffic Network during a Snowstorm

Road networks are the backbones of urban communities and can be vulnerable to many disruptions caused by natural hazards, crashes, and rush hours, etc. Numerous efforts have been made in recent years to building more resilient urban road traffic systems against various disruptions through an improved understanding of resilience performance and more effective traffic intervention. A new resilience modeling methodology has been developed for disrupted traffic systems under natural hazards in terms of both local traffic performance and percolation-based robustness at the network scale. Hybrid data enhancement is conducted by integrating limited real-world data and microscopic traffic simulation to address the common challenge of data scarcity under hazards. The study further investigates the feasibility of applying early active traffic intervention through traffic signal design optimization only at some strategic intersections to improve both the traffic performance and resilience at local and network scales. A case study of the City of Fort Collins during snowstorms is made to demonstrate the methodology. The contributions of this study are reflected by shedding light on the following critical, yet unanswered questions: (1) how to deal with the common data shortage of disrupted traffic systems under hazards, (2) how to provide comprehensive time-progressive resilience assessment of a disrupted traffic system at different spatial scales, and (3) is it feasible to apply traffic intervention, such as smart intersection traffic control, only at limited intersections during hazards to improve both system resilience and traffic performance.