GIS techniques to analyze factors associated with crash occurrence

Sagar, Shraddha; Stamatiadis, Nikiforos · 2022 · OpenAlex-citations

DOI: 10.55329/wmfk3422

archive: archived pipeline: cataloged verified

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Summary

This study investigates the relationship between driver socioeconomic characteristics and crash occurrence in Kentucky, a state with fatality rates exceeding the national average. Motivated by prior research suggesting that regional socioeconomic conditions, particularly in the Appalachian region, contribute to higher crash rates, the authors aim to identify at-risk driver groups and geographic areas. The research focuses on how factors such as income, age, and gender influence crash propensity, utilizing a spatial analysis approach to visualize these disparities across the state. The methodology employs crash data from 2013 to 2016 obtained from the Kentucky State Police, combined with census data. The study uses the quasi-induced exposure technique to estimate crash risk, assuming that not-at-fault drivers in two-vehicle crashes represent a random sample of the driving population. This allows for the calculation of the Relative Accident Involvement Ratio (RAIR), defined as the ratio of at-fault to not-at-fault drivers within specific demographic subgroups. RAIRs were calculated at the zip code level for drivers categorized by age (<25, 25–64, >64) and gender. Using GIS tools, these ratios were proportionally allocated and aggregated to the county level, weighted by population. Finally, the county-level RAIRs were normalized by median household income to create choropleth maps highlighting spatial variations in crash risk. The results indicate that young (<25) and older (>64) drivers have a higher propensity to be at-fault in two-unit crashes compared to middle-aged drivers. Male drivers consistently exhibit higher crash involvement ratios than females. Crucially, the analysis reveals that drivers residing in Appalachian counties, which are predominantly low-income areas, have a significantly higher crash propensity regardless of age or gender. For single-unit crashes, young drivers in higher-income counties showed higher risk, a trend attributed to potential data limitations or vehicle access, whereas middle-aged and older drivers in Appalachia maintained higher risk levels. The choropleth maps effectively visualized these high-risk zones, correlating elevated crash involvement with lower socioeconomic status. The significance of this study lies in its application for targeted safety interventions. The findings support the use of GIS-based spatial analysis to identify high-risk counties for programs like the Federal Highway Administration’s Safety Circuit Rider (SCR) program. By pinpointing areas with elevated crash propensities linked to socioeconomic factors, policymakers can implement more efficient safety campaigns and countermeasures. The study concludes that while demographic factors like age and gender influence crash risk, regional socioeconomic disparities, particularly in Appalachia, are critical determinants of crash occurrence. The methodology presented is adaptable for other regions with similar socioeconomic characteristics, offering a framework for data-driven traffic safety planning.

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.

Topics

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• demographic disparities
• incidence prevalence
• comparative international
• sex gender
• induced exposure

Information type

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• Empirical Findings: crash risk outcomes