Urban road network resilience assessment framework: Integrating spatiotemporal analysis with the resilience triangle and temporal performance indicators

Abstract

Daily non-recurrent disruptions such as traffic collisions can significantly degrade the performance of urban road networks yet remain underexamined in resilience studies. This paper presents a data-driven framework that captures the spatiotemporal propagation and dissipation of these disruptions and evaluates link-level resilience through two sets of metrics. The proposed methodology uses multi-year travel time and incident data to build a relative network performance function, employing a time-dependent network efficiency metric adapted from complex network theory and adjusted for daily and weekly traffic variations. It consists of two main components: a spatiotemporal analysis using an adaptive statistical threshold to estimate occurrence and restoration times of disruptions, and a resilience assessment module that applies both resilience triangle and novel temporal performance metrics. The framework is applied to a real-world case study in downtown Calgary. It reveals that disruptions often begin before they are reported and persist well beyond clearance times, highlighting the limitations of incident records in capturing the true extent of network impact. Findings also indicate that low-resilience links are spatially clustered in high-demand, low-redundancy areas, with resilience loss in this case primarily driven by prolonged recovery. Incorporating temporal performance metrics further reveals degradation and recovery patterns, early drops, delayed recovery, and abrupt, symmetric transitions, that are not captured by traditional measures, offering deeper insight into timing and nature of performance changes. The proposed approach offers transportation agencies a multidimensional tool to better understand the resilience of urban road networks and guide targeted operational responses for daily disruptions.