  We investigate the spatiotemporal dynamics of traffic accidents in Addis
Ababa, Ethiopia, using 2016--2019 data. We formulate the traffic accident
intensity as a log-Gaussian Cox Process and model it as a spatiotemporal point
process with and without fixed and random effect components that incorporate
possible covariates and spatial correlation information. The covariate includes
population density and distance of accident locations from schools, from
markets, from bus stops and from worship places. We estimate the posterior of
the state variables using integrated nested Laplace approximations with
stochastic partial differential equations approach by considering Mat\`ern
prior. Deviance and Watanabe - Akaike information criteria are used to check
the performance of the models. We implement the methodology to map traffic
accident intensity over Addis Ababa entirely and on its road networks and
visualize the potential traffic accident hotspot areas. The comparison of the
observation with the model output reveals that the covariates considered has
significant effect for the accident intensity. Moreover, the information
criteria results reveal the model with covariate performs well compared with
the model without covariates. We obtained temporal correlation of the
log-intensity as 0.78 indicating the existence of similar traffic fatality
trend in space during the study period.
