Numerous road safety studies have been dedicated to the estimation of crash frequency and injury severity models. However, previous research has shown that different factors may influence the occurrence of crashes of different types. In this study, a dataset including information from crashes occurred at segments and intersections of urban roads in Bari, Italy was used to estimate the likelihood of occurrence of various crash types. The crash types considered are: single-vehicle, angle, rear-end and sideswipe. Models were estimated through a mixed logit structure considering various crash types as outcomes of the dependent variable and several traffic, geometric and context-related factors as explanatory variables (both siteand crash-specific). To account for systematic, unobserved variations among the crashes occurred on the same segment or intersection, the grouped random parameters approach was employed. The latter allows the estimation of segmentor intersection-specific parameters for the variables resulting in random parameters. This approach allows assessing the variability of results across the observations for individual segments/intersections.Segment type and the presence of bus lanes were included as explanatory variables in the model of crash types for segments. Traffic volume per entering lane, total entering lanes, total number of zebra crossings and the balance between major and minor traffic volumes at intersections were included as explanatory variables in the model of crash types for intersections. Area type was included in both segment and intersection models. The typical traffic at the moment of the crash (from on-line traffic prediction tools) and the period of the day were associated with different crash type likelihoods for both segments and intersections. Significant variations in the effect of several predictors across different segments or intersections were identified. The applicability of the study framework is demonstrated, in terms of identifying roadway sites with anomalous tendencies or high risk sites with respect to specific crash types. (C) 2020 Elsevier Ltd. All rights reserved.
