Citation

Li X, Yu S, Huang X, Dadashova B, Cui W, Zhang Z. Accid. Anal. Prev. 2022; 173: e106721.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.aap.2022.106721

PMID

35659647

Abstract

Understanding the relationship between social vulnerability and traffic crashes is a cornerstone for promoting social justice in transportation planning and policymaking. However, few studies have examined the disparities in traffic crashes by systemically considering the influence of social vulnerability via spatial analysis approaches. This study puts forward a new approach to assess the inequity in transportation safety by spatially examining the relationships between crash risks and the social vulnerability index (SVI) established by the Centers for Disease Control and Prevention (CDC). We performed spatial autocorrelation analyses to identify the clusters of high-risk and high-vulnerable census tracts in Texas. Meanwhile, we innovatively applied the Multiscale Geographically Weighted Regression model (MGWR) to assess the impacts of CDC SVI on crash risks spatially and statistically. The results demonstrate that the crash rate and the social vulnerability are significantly correlated in the highly urbanized regions as well as the southern border along the Rio Grande in Texas. The MGWR results indicate the minority status of census tracts is strongly correlated with overall crashes in north-central and northeastern Texas, and the socioeconomic status is tightly correlated with fatal crashes across Texas. The outcomes from this study have significant implications for transportation planning and policymaking.

Language: en

Keywords

Social vulnerability; Crash risks; Inequity assessment; MGWR; Spatial autocorrelation analysis