Examining the macro-level factors affecting vehicle breakdown duration

Abstract

A substantial part of traffic congestion is triggered by unplanned incidents such as crashes, breakdowns and hazards, reducing road capacity and increasing the delays, pollution, and productivity losses. Previous studies on incident duration have focussed on individual incidents and the influencing factors that could be obtained directly from the incident description. Consequently, the explanatory variables were more localized, and the impacts of broader macro-level factors were not explored. This contrasts with the studies on incident frequency, where the influencing factors are typically collected at a macro-level. Therefore, this study aims to explore the impact of various factors associated with reported vehicle breakdown duration at a macro-level. Street network characteristics such as connectivity, density, and hierarchy were included as covariates, in addition to the demographic, vehicle utilization, and environmental variables. The dataset contains over 72,000 vehicle breakdowns records within 4.5 years (January 2012 to June 2016) in Greater Sydney, Australia involving 44 SA3s (Statistical Area Level 3). After a principal component dimension reduction of independent variables, a fixed-parameters accelerated failure time (AFT) hazard-based model with underlying log-logistic, log-normal and Weibull distributions were used in this analysis. Weibull hazard distribution with gamma frailty and the latent class models were also considered to account for unobserved heterogeneity. The latent class model provides the best fit where road network connectivity, hierarchy, and familiarity factors are considered to have both positive and negative impact on duration; higher road network density, mixed land-use, and spatial disorientation of roads are associated with longer duration; and higher income and exposure (vehicle kilometres travelled) are associated with shorter duration. The results will help incident management agencies to better allocate current response resources and predict the resources required in the future. Besides, the results associated with network structure measures can provide valuable insights to community planning authorities to manage unplanned congestion.