Road networks are often subjected to disruptions caused by demand and capacity uncertainties, leading to excessive delays. A resilient transport system could absorb and recover quickly from such events. However, resiliency varies across different links, which may be because of zonal characteristics, network structure, or other factors. This study develops a methodology to quantify road network resilience at the zonal level and identify factors affecting it. Crowdsourced traffic speed data from approximately 33,000 locations was used to calculate resilience metrics, including the zonal resilience index, zonal vulnerability index, and zonal recoverability index. These metrics were modeled using geographically weighted regression to explore their relationship with independent variables. The results revealed that zonal trip heterogeneity, land use heterogeneity, and road category heterogeneity within a zone significantly reduce resilience. In contrast, connectivity measures, such as the clustering and degree assortativity coefficients, improve the recoverability of the zone. The increase in households owning more than two motorized vehicles in a zone reduces zonal resilience. The models were validated using subsets of the data, splitting weekdays from June 1–15 and June 16–30 and testing the model under different zone sizes. Results showed consistent variable effects across subsets and configurations, with slight variations in the significance of certain factors. Policymakers can utilize these insights to create land use or congestion pricing policies for individual zones to curb congestion. In addition, the network topology results can help plan a resilient road network for developing cities.
Lalwani et al. (Fri,) studied this question.