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Despite the increase of these non-motorized trips, bicyclists and pedestrians remain vulnerable road users that are often over represented in traffic crashes. While the currently used methods that identify hazardous locations serve their purpose well, majority represent a reactive approach that seeks improvement after crashes happen. This research addressed these issues and proposed decision support tools to aid the implementation of bicycle and pedestrian safety strategies. This work developed an access based tool to predict the expected number of crashes at different neighborhood levels. This tool combines the traditional methods such as those provided in the Highway Safety manual to predict the expected number of bicycle and pedestrian crashes. First, a cluster analysis technique is proposed and developed a Geographic Information Systems (GIS) technique to facilitate the identification of high crash locations. Safety Performance Functions (SPFs) are developed in form of mathematical equations to relate the number of crashes to area socioeconomic and demographic characteristics. An integrated system consisting of access database and safety performance functions, and whose interface is designed to automatically compute the number of crashes given the input values is developed. Basing on crash value, the tool can be adopted as a framework to guide the appropriate allocation of safety improvement resources.

ID Number

TRCLC 14-07