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Traffic safety is a major concern in the transportation industry due to immense monetary and emotional burden caused by crashes of\udvarious severity levels, especially the injury and fatality ones. To reduce such crashes on all public roads, the safety management\udprocesses are commonly implemented which include network screening, problem diagnosis, countermeasure identification, and\udproject prioritization. The selection of countermeasures for potential mitigation of crashes is governed by the influential factors\udwhich impact roadway crashes. Crash prediction model is the tool widely adopted by safety practitioners or researchers to link\udvarious influential factors to crash occurrences. Many different approaches have been used in the past studies to develop better\udfitting models which also exhibit prediction accuracy. In this study, a crash prediction model is developed to investigate the\udvehicular crashes occurring at roadway segments. The spatial and temporal nature of crash data is exploited to form a spatiotemporal\udmodel which accounts for the different types of heterogeneities among crash data and geometric or traffic flow variables. This study\udutilizes the Poisson lognormal model with random effects, which can accommodate the yearly variations in explanatory variables\udand the spatial correlations among segments. The dependency of different factors linked with roadway geometric, traffic flow, and\udroad surface type on vehicular crashes occurring at segments was established as the width of lanes, posted speed limit, nature of\udpavement, and AADT were found to be correlated with vehicle crashes

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

INVESTIGATION OF ROADWAY GEOMETRIC AND TRAFFIC FLOW FACTORS FOR VEHICLE CRASHES USING SPATIOTEMPORAL INTERACTION