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Description

The report presents a comprehensive analysis addressing safety equity concerns for rural communities and American Indian and Alaskan Native (AI/AN) populations, who experience disproportionate rates of serious injuries, fatalities, and general collisions. Despite these disparities, significant gaps exist in understanding the demographics of collisions, particularly within tribal communities where law enforcement jurisdictions are complex, and individuals may misreport their tribal status to gain benefits, leading to biases in collision data. This study aims to fill these gaps by developing a statistical model to predict the true demographics of collisions and enhance safety equity. An ecological regression model, accounting for individual-level characteristics influencing collision rates, is employed. Focusing on Yakima County, Washington—a rural area with the large Yakama Nation reservation—the study examines the impact of household income and AI/AN status on collision rates across three categories: all collisions, injury collisions, and fatal collisions. The results reveal that lower-income individuals are slightly overrepresented in collisions, while higher-income individuals are underrepresented. However, AI/ANs are significantly overrepresented in all collision types, being 3.8 times more likely to be involved in fatal collisions compared to the general population. These findings highlight the utility of ecological regression in revealing the true demographics of collisions and underscore critical safety equity issues in rural and AI/AN communities.

Publication Date

7-17-2024

Keywords

Safety Data Tool, Roadway Safety Assessment, Pedestrian safety, collisions, severity, HSS database, Machine Learning, statistical modeling, classification methods

Handle

http://hdl.handle.net/11122/15264

Comprehensive Transportation Equity Analysis for RITI Community: A Data-Driven Approach with Case Study

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