Pedestrian Crash Exposure Analysis Using Alternative Geographically Weighted Regression Models

In order to develop a sustainable, safe, and dynamic transportation system, proper attention must be paid to the safety of pedestrians. The purpose of this study is to analyze the surrogate measures related to pedestrian crash exposure in urban roads, including the use of sociodemographic characteristics, land use, and geometric characteristics of the network. This study develops pedestrian exposure models using geographical spatial models including geographically weighted regression (GWR), geographically weighted Poisson regression (GWPR), and geographically weighted Gaussian regression (GWGR). In general, the results of the GWPR model show that the presence of a bus station, population density, type of residential use, average number of lanes, number of traffic control cameras, and sidewalk width are negatively associated with increasing the number of crashes. In this study, in order to identify traffic analysis zones (TAZ) based on the observed and predicted crash data, spatial distance-based methods using GWPR outputs have been used. This study shows the dispersion and density of pedestrian crashes without possessing the volume of pedestrians. Comparison of the performance of GWPR and Poisson models shows a significant spatial heterogeneity in the analysis.

Automated summary

This study analyzes pedestrian crash exposure in urban roads using surrogate measures including sociodemographic characteristics, land use, and geometric characteristics of the network. Pedestrian exposure models are developed using geographical spatial models such as GWR, GWPR, and GWGR. The results show negative associations between factors like bus stations, population density, residential use, number of lanes, traffic control cameras, and sidewalk width with increasing crash numbers. Analysis using GWPR outputs reveals spatial heterogeneity in pedestrian crash data.