RESUMEN
Traffic crash risk prediction models have been developed to investigate crash occurrence mechanisms and analyze the effects of various traffic operation factors, data on which are collected by densely deployed detectors, on crash risk. However, in China, freeway detectors are widely spaced (the spacing is usually more than 2 km) and the road geometries vary frequently, especially in mountainous areas. Moreover, many freeway sections are located in urban areas and serve commuting functions. Due to the different mechanisms of crash occurrence on road segments with different geometric design features and traffic operation status, it is necessary to consider these heterogeneities in crash risk prediction. In addition to considering observable heterogeneous effects, it is equally important to consider the existence of unobserved heterogeneities among crash units. This study focuses on the effects of different types of heterogeneities on crash risk for segments of the Yongtaiwen Freeway in Zhejiang Province, China, using crash, traffic flow, and road geometric design data. Latent class analysis (LCA), latent profile analysis (LPA), and a combination of both methods are respectively used to classify road segments into subgroups based on road geometric design features, the traffic operation status, and a combination of both. The results reveal that the binary logit model considering the heterogeneous effects of the combination of road geometric design features and the traffic operation status achieves the best performance. Furthermore, binary conditional logit models and grouped random parameter logit models are developed to analyze the unobserved heterogeneity among crash units, and the results show that the latter has a better goodness of fit. Finally, a paradigm of the crash risk prediction for freeway segments with widely-spaced traffic detectors and frequently-changing geometric features is provided for traffic safety management departments.