Combinational impacts of emergency medical service response and transport times on traffic crash injuries: A nonlinear-pattern modeling with urban–rural heterogeneity

Emergency medical services (EMS) play a critical role in reducing injury severity and improving survival following traffic crashes, yet the effects of different prehospital time stages remain insufficiently understood. Using national crash data from the U.S. Fatality Analysis Reporting System (FARS) for 2022–2023, this study examines the effects of EMS response and transport times on traffic injury severity. Logistic regression and restricted cubic spline (RCS) models are employed to capture nonlinear associations, and urban–rural stratified analyses further assess spatial heterogeneity. By separating response and transport times and comparing their effects across urban and rural settings, this study provides a more detailed understanding of how prehospital time influences traffic injury severity. After controlling for individual characteristics, vehicle attributes, crash types, and environmental conditions, the results reveal significant nonlinear relationships between EMS times and severe injury risk. Response time shows a more pronounced nonlinear effect in urban areas, whereas transport time plays a stronger role in rural areas. Several critical time thresholds are also identified. These findings suggest that EMS optimization should move beyond minimizing prehospital time alone and incorporate injury severity profiles, nonlinear temporal effects, and spatial context to support targeted ambulance dispatch and emergency resource allocation strategies.