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This paper investigates the inclusion of spatial effects in accident prediction models. Two types of spatial modeling techniques–-the Gaussian conditional autoregressive (CAR) and the multiple membership (MM) models–-were compared with the traditional Poisson–lognormal model. A variation of the MM model (extended MM or EMM) was also investigated to study the effect of clustering segments within the same corridor on spatial correlation. Full Bayes estimation was used by means of the Markov chain Monte Carlo methodology to estimate the parameters. The study made use of 281 urban road segments in Vancouver, British Columbia, Canada. Various traffic and geometric variables were included in the accident prediction models. The models were compared in terms of their goodness of fit and inference. For the data set under consideration, the results showed that annual average daily traffic, business land use, the number of lanes between signals, and the density of unsignalized intersections have significant positive impact on the number of accidents. The fitted CAR and MM models had significant estimates for both heterogeneity and spatial correlation parameters. The best-fit model was EMM, followed by CAR. Furthermore, a significant portion of the total variability was explained by the spatial correlation. A significant correlation was also found between the heterogeneity and spatial effects. This may be because neighboring road segments typically have similar environmental and geographic characteristics and thereby form a cluster with similar accident occurrence. The results also showed that corridor variation was a major component of total variability and that the spatial effects have been considerably alleviated by clustering segments within the same corridor.