Spatial analysis of collision risk of child pedestrians – A case of urban elementary school districts in Busan, Korea

• Published in: Travel, behaviour & society Vol. 29; pp. 165 - 175
• Main Authors: Kim, Hyunsoo, Jang, Jinhyeok, Choi, Yeol
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2022
• Subjects: Built environment; Child pedestrians; Geographically-weighted negative binomial regression; Pedestrian safety; Spatial collision risk modeling; Urban school districts; Pedestrian safety; Built environment; Urban school districts; Child pedestrians; Spatial collision risk modeling; Geographically-weighted negative binomial regression
• ISSN: 2214-367X
• DOI: 10.1016/j.tbs.2022.06.008

•The safety of child pedestrians was analyzed for urban elementary school districts.•Collision risk analysis utilized both statistical and spatial modeling techniques.•The collision-inducing factors for children and their spatial effect were concerned.•The safety of child pedestrians is a critical issue in vulnerable neighborhoods.•Urban areas with dense private educations need a secure child pedestrian space. A comprehensive understanding of the relationship between pedestrian collision risks for children and the environment is crucial for planning safety policies. Although pedestrian-vehicle collisions are an extensively studied subject in many cities, only a handful of studies focus on children. Previous studies that examined the relationship between pedestrian collision risk for children and environmental factors primarily relied on statistical models, which were limited from accounting for the relationship's spatial heterogeneity. This study takes both statistical and spatial modeling techniques to understand the geography of pedestrian collision risk for children in Busan, Korea. Namely, negative binomial regression and geographically-weighted negative binomial regression were used in modeling the relationship between child pedestrians' collision risk and various environmental factors. Utilizing the collision data from 2014 to 2019, a total of 788 collisions in 297 elementary school districts were analyzed with a set of socioeconomic, transportation, and land-use characteristics. The study found that the geographically-weighted negative binomial regression outperform the negative binomial regression, while both models provided informative results toward characterizing the collision risk of child pedestrians by identifying a set of collision-inducing factors from both global and local perspectives. This study shows that geographically-weighted negative binomial regression is a promising tool for designing safety policies accounting for different regional contexts in various cities. This study found that collision risk varies by school district and local characteristics, thus, a collaborative network between city planners, schools, and local communities is needed to create a safer walking environment for children.