Mechanism analysis of traffic accident prone points based on The Spatial Durbin Model

Abstract

The significant increase of freight traffic related collision accidents has aroused people's increasing concern about road safety. Using data from California, this paper studies the spatial relationship between freight related traffic accidents and low-income and minority communities. The study found that household income and minority population were significantly correlated with the density of freight related crashes and freight related crashes that led to serious casualties. Compared with areas with high-income groups, areas with low economic status, such as blacks and Asians, are more than 119% more likely to have traffic accidents. Among them, areas with black populations are more than 39% more likely to have traffic accidents than areas with Asian populations. The results show that freight related collisions are affected by spatial inequality.

Analyzing urban crash incidents: An advanced endogenous approach using spatiotemporal weights matrix

“…More recently, Gilardi et al (2023) developed a contiguity-based SWM and utilized a Bayesian Hierarchical Model for crash analysis. Xiong et al (2023) developed an SWM based on the distances between census areas and applied a spatial Durbin model to extract the spatial relationship between traffic accidents and low-income and minority communities. Wu et al (2024) utilized a decay function to define an exogenous SWM within a macro-level Middle-Super-Output-Area (MSOA), which is delineated as a spatial unit averaging 8000 inhabitants and used a spatial Random Forest to predict crash incidents.…”

“…Traffic characteristics • Average Annual Daily Traffic (AADT) (Alarifi et al, 2018;Huang et al, 2016;Liu et al, 2017;Mahmud et al, 2019;Xiong et al, 2023) • Vehicle Miles Traveled (VMT) (Xu et al, 2019) • Running red lights (Retting et al, 1999) • Street level (Alarifi et al, 2018) • Zonal level (Huang et al, 2016;Liu et al, 2017;Mahmud et al, 2019;Retting et al, 1999;Xiong et al, 2023;Xu et al, 2019) Road characteristics • One-way streets, bus and bike lanes, road quality (WHO, 2018) • Speed limit (Almasi & Behnood, 2022;Huang et al, 2016;Liu et al, 2017;Ma et al, 2017;Mahmud et al, 2019;Rahman et al, 2023) (Alarifi et al, 2018;Huang et al, 2016;Liu et al, 2017;Xie & Yan, 2008) • Proximity to intersections (Li et al, 2019) • Number of intersections (Hasan et al, 2022;Shariat-Mohaymany et al, 2015) • Road type (Hasan et al, 2022;Huang et al, 2016;Li et al, 2019;Wu et al, 2024) • Number of road lanes (Alarifi et al, 2018;Huang et al, 2016;Ma et al, 2017) • Presence of a median on roads (Alarifi et al, 2018;Huang et al, 2016) • Vertical grade, curvature of roads (Wen et al, 2019) • Pavement condition (Huang et al, 20...…”