Affiliation: College of Arts and Sciences, Department of City and Regional Planning
For Black Americans, the risk of being a victim of traffic violence while walking or biking is higher than it is for the general public. However, for local and regional governments, racial crash disparities are not well documented, and existing methods for addressing racial crash disparities are not widespread. Consequently, the purpose of this report is to provide an example of racial crash disparities at the regional level, and to test the effectiveness of an existing method used to address racial differences in crashes. Wake County, NC was selected as the analysis region for two reasons: the robust pedestrian and bicycle crash data publicly available, and the lack of existing analysis on pedestrian and bicyclist crashes by race. The ‘High Priority Network’ method for addressing racial disparities is the most popular existing model, and it can be easily modified for different regions. The Portland Vision Zero ‘High Priority Network’ model is a prominent version of this model; thus, it was applied and tested in Wake County. Its three main components—Communities of Concern, High Crash Roads, and High Crash Intersections—were analyzed individually.
The analysis revealed that the overall rates of crashes were considerably higher for Black pedestrians and bicyclists, as were the median crash rates by Census Tract. Additionally, Black pedestrians and bicyclist crash victims had consistently less access to infrastructure at the location of the crash. When applied to Wake County, the Portland model for High Priority Networks was fairly competent at locating areas within Wake County with high numbers of Black crashes and a high rate of Black crashes. By modifying the network to focus on racial metrics, the model was more effective at addressing areas of high racial disparity. While some of the racial metrics were less effective at addressing all crashes within the system, a model which combines the standard metrics used by Portland and racial-specific metrics may results in better equity outcomes while not sacrificing the overall efficacy of the model.