Traffic crash hot spot analyses allow identification of roadway segments that may be of safety concern. Understanding geographic patterns of existing motor vehicle crashes is one of the primary steps for geostatistical-based hot spot analysis. Much of the current literature, however, has not paid particular attention to differentiating among cluster types based on crash severity levels. This study aims at building a framework for identifying significant spatial clustering patterns characterized by crash severity and analyzing identified clusters quantitatively. A case study using an integrated method of network-based local spatial autocorrelation and the Kernel density estimation method revealed a strong spatial relationship between crash severity clusters and geographic regions. In addition, the total aggregated distance and the density of identified clusters obtained from density estimation allowed a quantitative analysis for each cluster. The contribution of this research is incorporating crash severity into hot spot analysis thereby allowing more informed decision making with respect to highway safety.

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The objectives of this research were to test and validate the feasibility of assessing humped highway–rail crossings for safe passage of vehicles with low ground clearance using lidar data. Collected using an airborne platform, lidar data provide georeferenced spatial information about the shape and surface characteristics of Earth. The suitability of humped highway–rail crossings for use by vehicles with low ground clearance and a long wheelbase is a concern because of the possibility of vehicles getting lodged on rail tracks. While such vehicles usually travel on designated routes, emergencies or highway closures may result in these vehicles traveling on undesignated highways with humped grade crossings. Lidar data and line-of-sight analysis in a geographic information system (GIS) were used to identify potentially problematic grade crossings for certain types of low-ground-clearance vehicles with a long wheelbase. Results of the GIS analysis were validated in the field at actual grade crossings with survey equipment. The main conclusion was that lidar data could be successfully used for identification of highway–rail crossing vehicle hang-up issues.

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