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

Spatio-temporal modeling of traffic risk mapping on urban road networks

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesOver the past few years, traffic collisions have been one of the serious issues all over the world. Global status report on road safety, reveals an increasing number of fatalities due to traffic accidents, especially on urban roads. The present research work is conducted on five years of accident data in an urban environment to explore and analyze spatial and temporal variation in the incidence of road traffic accidents and casualties. The current study proposes a spatio-temporal model that can make predictions regarding the number of road casualties likely on any given road segments and can generate a risk map of the entire road network. Bayesian methodology using Integrated Nested Laplace Approximation (INLA) with Stochastic Partial Differential Equations (SPDE) has been applied in the modeling process. The novelty of the proposed model is to introduce "SPDE network triangulation" precisely on linear networks to estimate the spatial autocorrelation of discrete events. The result risk maps can provide geospatial baseline to identify safe routes between source and destination points. The maps can also have implications for accident prevention and multi-disciplinary road safety measures through an enhanced understanding of the accident patterns and factors. Reproducibility self-assessment : 3, 1, 1, 3, 2 (input data, preprocessing, methods, computational environment, results)