Traffic behaviour and network resilience in extreme weather: A state of the art review

The current study presents a systematic review aimed at understanding and incorporating the extensive body of literature to describe and assess the wide-ranging impacts of various weather conditions, including heatwaves, fog, snow, and rain, and the different ways of using high-fidelity simulators, empirical driving studies, and intricate econometric and deep causal inference frameworks. The review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to ensure a transparent and rigorous study selection process. The reviewed literature consistently indicates that precipitation-related weather conditions substantially increase crash frequency and severity, although the magnitude of impact varies across locations, roadway types, and traffic conditions. At the operational level, these weather conditions deteriorate traffic performance by reducing speed, roadway capacity, and overall network efficiency. These network-level phenomena are mediated by micro-level phenomena of driving behaviour, e.g. abnormal speed choice, altered car following, and high cognitive load. Connected vehicle systems and variable speed limits are two technological countermeasures that may help reduce the risks. In the end, these integrations highlight the relationship among variables of traffic and weather, as variables and context depend. Lastly, 2 main research gaps still need to be addressed, which include the lack of use of causal inference models and the limited Real-World Application and Validation of Advanced Traffic-Weather Models. Overall, the study's findings suggest the need for adaptable, data-centric, and context-sensitive safety and traffic management strategies to enhance the resiliency of the transportation network.