Intersections and Non-Intersections: A Protocol for Identifying Pedestrian Crash Risk Locations in GIS

Intersection and non-intersection locations are commonly used as spatial units of analysis for modeling pedestrian crashes. While both location types have been previously studied, comparing results is difficult given the different data and methods used to identify crash-risk locations. In this study, a systematic and replicable protocol was developed in GIS (Geographic Information System) to create a consistent spatial unit of analysis for use in pedestrian crash modelling. Four publicly accessible datasets were used to identify unique intersection and non-intersection locations: Roadway intersection points, roadway lanes, legal speed limits, and pedestrian crash records. Two algorithms were developed and tested using five search radii (ranging from 20 to 100 m) to assess the protocol reliability. The algorithms, which were designed to identify crash-risk locations at intersection and non-intersection areas detected 87.2% of the pedestrian crash locations (r: 20 m). Agreement rates between algorithm results and the crash data were 94.1% for intersection and 98.0% for non-intersection locations, respectively. The buffer size of 20 m generally showed the highest performance in the analyses. The present protocol offered an efficient and reliable method to create spatial analysis units for pedestrian crash modeling. It provided researchers a cost-effective method to identify unique intersection and non-intersection locations. Additional search radii should be tested in future studies to refine the capture of crash-risk locations.

Neighbourhood Influences on Vehicle-Pedestrian Crash Severity

Socioeconomic factors are known to be contributing factors to vehicle-pedestrian crashes. Although several studies have examined the socioeconomic factors related to the locations of crashes, few studies have considered the socioeconomic factors of the neighbourhoods where road users live in vehicle-pedestrian crash modelling. In vehicle-pedestrian crashes in the Melbourne metropolitan area, 20% of pedestrians, 11% of drivers, and only 6% of both drivers and pedestrians had the same postcode for the crash and residency locations. Therefore, an examination of the influence of socioeconomic factors of their neighbourhoods, and their relative importance will contribute to advancing knowledge in the field, as very limited research has been conducted on the influence of socioeconomic factors of both the neighbourhoods where crashes occur and where pedestrians live. In this chapter, neighbourhood factors associated with road users' residents and location of crash are investigated using BDT model. Furthermore, partial dependence plots are applied to illustrate the interactions between these factors. The authors found that socioeconomic factors account for 60% of the 20 top contributing factors to vehicle-pedestrian crashes. This research reveals that socioeconomic factors of the neighbourhoods where road users live and where crashes occur are important in determining the severity of crashes, with the former having a greater influence. Hence, road safety counter-measures, especially those focussing on road users, should be targeted at these high-risk neighbourhoods.