Planning and location of resources for urban traffic management generate complex decision problems, given the uncertainty of variables that explain traffic behavior, the lack of data, and the large number of factors to be considered to create optimal policies. In particular, the attention to traffic-related accidents by local authorities requires the modeling and forecasting of events, spatially and temporally defined. In this study we use data from the traffic police department (Bogota) about incidents with injuries or fatalities (2013–2016). We locate each event in spatial coordinates and crossed the observations with exogenous variables (climate, seasonal and road properties). We model the spatiotemporal stochastic process for accidents using a Log-Gaussian Cox model given its flexibility as it enables the use of fixed and random effects. The results of this study permit the identification of factors that increase the risk of accidents, and location of critical zones in the city.