Exploring associations between built environment and crash risk of children in school commuting

Yaxin Wu; Xiaowei Hu; Xiaofeng Ji; Ke Wu

doi:10.1016/j.aap.2023.107287

Exploring associations between built environment and crash risk of children in school commuting

Accid Anal Prev. 2023 Dec:193:107287. doi: 10.1016/j.aap.2023.107287. Epub 2023 Sep 18.

Authors

Yaxin Wu¹, Xiaowei Hu², Xiaofeng Ji³, Ke Wu⁴

Affiliations

¹ School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China. Electronic address: 21b932020@stu.hit.edu.cn.
² School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China. Electronic address: xiaowei_hu@hit.edu.cn.
³ Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650504, China. Electronic address: yiluxinshi@sina.com.
⁴ Hongyousoft Co. Ltd, Karamay 834000, China.

PMID: 37729750
DOI: 10.1016/j.aap.2023.107287

Abstract

Understanding how built environment are associated with crash risk (CR) in school commuting is essential to improving travel safety through land use and transportation policies. Scholars often assume that this relationship is consistent across space, but this may lead to inconsistent estimates. To address this issue, using data in Shenzhen, China, the data covers traffic accident data of children taken from police incident reports and supplemented with local land use, transportation network and specific school information. The measurement model of crash scale was conducted to represent crash severity, and the CR was further quantified. The study applies three models, spatial dubin model (SDM), geographically weighted regression (GWR), and mixed GWR (MGWR), to explore spatio-temporal heterogeneity relationships between built environment attributes and CR of children in school commuting. The findings reveal that the crash scale can better represent crash severity of school commuting than a single indicator. Policy interventions should be targeted at specific spatial scales, school types, and time windows to effectively improve travel safety. However, there are some common findings. It is recommended to use a scale of 200 m to explain the relationship between the variables. The MGWR model outperforms the other two models. To reduce CR, it is important to consider lower road network density, a reasonable layout of educational facilities, fewer bus routes, and more on-street parking spaces. Our findings can help to enrich the understanding of associations between land use and CR of children, as well as offer local planning and operating guidance for creating child-friendly environment.

Keywords: Built environment; Children; Crash risk in school commuting; Crash scale; Mixed geographically weighted regression; Spatio-temporal heterogeneity.