Spatiotemporal Risk Mapping of Statewide Weather-related Traffic Crashes: A Machine Learning Approach

Abstract

Improving transportation safety statewide is key in upholding a state's economy. However, weather-related crashes, known to be one of the most severe types of crashes, poses a threat to this as lots of money is lost to lives and property damage. The goal of this study is to employ machine learning (ML) to develop a workflow on which weather-related crash risk can be better identified, predicted, and interpreted. Central to this workflow, the effects of spatiotemporal heterogeneity of weather-related crashes are studied. To demonstrate the workflow, weather-related crash events in the state of North Carolina were used. Space-time cubes were created using an optimized 5 mi x 5mi grid size and 1-month time aggregation. Equivalent property damage only (EPDO) scores were computed for each space-time cube to create a risk metric that combines both crash frequency and severity. A two-layered technique was employed for identifying and labelling crash risk zones. Subsequently, XGBoost model was used to predict crash risk zones and identify factors associated with the different risk levels. SHapley Additive exPlanations (SHAP), an explainable AI (XAI) tool, was used to interpret the model and examine the relationship between the explanatory variables and the outcome. Per the results, there are three optimal clusters with distinct variability of the impact of weather conditions that constitute the crash risk levels in the study area. The workflow can be used by transportation safety units within state departments of transportation (DOTs) to evaluate different safety risk levels, and the potential high-risk zones can be flagged for devising countermeasures (i.e., proactive risk mitigation strategies).