Do underserved and socially vulnerable communities observe more crashes? A spatial examination of social vulnerability and crash risks in Texas

Li, Xiao; Yu, Siyu; Huang, Xiao; Dadashova, Bahar; Cui, Wencong; Zhang, Zhe

doi:10.1016/j.aap.2022.106721

Cited by 17 publications

(2 citation statements)

References 37 publications

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“…In the Minneapolis St. Paul metropolitan area, including seven counties and two core cities of Minneapolis and St. Paul, household income and the percentage of minority population are significantly correlated with the density of freight related crashes and freight related crashes that cause serious casualties [14]. The situation of ethnic minorities in the census area is closely related to the overall traffic accidents in North Central and northeast Texas, while the socio-economic situation is closely related to the fatal traffic accidents in the state [15].…”

Section: Literature Reviewmentioning

confidence: 97%

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

Xiong¹,

Zhang²,

Wu³

2023

HSET

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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.

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Section: Literature Reviewmentioning

confidence: 97%

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

Xiong¹,

Zhang²,

Wu³

2023

HSET

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“…Tere is no reference value of Moran's I that indicate autocorrelation, but a signifcant p value indicates the relevance of autocorrelation. Several previous studies reported a moderately high Moran's I of 0.32 [30] or 0.30 [31] at the signifcance level of 1%, but some studies reported a low Moran's I in the range of 0.1-0.2 [32] or less than 0.1 [33,34]. Global spatial autocorrelation is useful when identifying the overall spatial correlation of certain areas presented within the research scope, but there are also limitations.…”

Section: Spatial Autocorrelationmentioning

confidence: 98%

Analysis of Hotspots in and outside School Zones: A Case Study of Seoul

Chun,

Lim,

Kim

2024

Journal of Advanced Transportation

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With growing social concern on pedestrian accidents involving children, the Korean government announced a plan to decrease the number of child deaths due to traffic accidents by 2026. Therefore, policymakers should consider various measures for school zones because a safe school walkway is essential for preventing traffic accidents around schools. Some parts of the roads within a radius of 300 m from elementary school and kindergarten entrances are designated as school zones. Certain roads experience frequent accidents within the school zone, while others experience frequent accidents outside the school zone. Hence, this study aimed to provide school zone types in Seoul by noting different occurrence accidents within and outside each school zone and suggest proper countermeasure by type. After selecting a 300 m radius analysis unit from the school zones, a distinction was made between the school zones and outside for each analysis unit. After verifying the spatial autocorrelation in each unit, hotspot analysis identified four types based on the presence or absence of hotspots in each unit. Types were defined as follows: Type A—no hotspots in school zones or outside the school zones; Type B—hotspots only outside the school zones; Type C—hotspots only the school zones; and Type D—hotspots both in school zones and outside the school zones. Subsequently, a case study was conducted to validate the types. For Types B and C, the results revealed differences in the installation of traffic safety facilities and the environment between within and outside the school zones. Therefore, Type B requires improving safety outside the school zones by expanding school zones to match the safety level within. For Type C, it implies the need to strengthen safety measures in the school zones. Lastly, for Type D, improvement projects for a safe walking environment should be implemented in primarily by conducting separate inspections.

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Spatial analysis of moving-vehicle crashes and fixed-object crashes based on multi-scale geographically weighted regression

Tang

Wang

2023

Accident Analysis & Prevention

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Do underserved and socially vulnerable communities observe more crashes? A spatial examination of social vulnerability and crash risks in Texas

Cited by 17 publications

References 37 publications

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

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

Analysis of Hotspots in and outside School Zones: A Case Study of Seoul

Spatial analysis of moving-vehicle crashes and fixed-object crashes based on multi-scale geographically weighted regression

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