Spatiotemporal modeling of traffic risk mapping: A study of urban road networks in Barcelona, Spain

Accidents on the road have always been a major concern in modern society. According to World Health Organization, globally traffic collisions are one of leading and fastest growing causes disability death. The present research work is conducted ten years accident data an urban environment explore analyze spatial temporal variation accidents related injuries. proposed spatiotemporal model can make predictions regarding number injuries incurred individual segments. Bayesian methodology using Integrated Nested Laplace Approximation (INLA) with Stochastic Partial Differential Equations (SPDE) has applied generate predicted risk map for entire network. current study introduces INLA- SPDE modeling perform predictive analysis selected areas, precisely networks instead traditional continuous regions. Additionally, result maps act as baseline identify safe routes context. be adapted enhanced INLA-SPDE point processes networks.