Developing a Conceptual Tribal Crash Safety Dashboard: Data-Driven Strategies for Identifying High-Risk Areas and Enhancing Tribal Safety Programs
Tianyi Chen, Haotian Shi, Steven T. Parker, Glenn Vorhes, David A, Noyce, Bin Ran

TL;DR
This paper presents a data-driven tribal crash safety dashboard prototype that identifies high-risk areas and analyzes crash patterns to improve safety programs on tribal lands, demonstrated with Wisconsin data.
Contribution
It introduces a generic, adaptable crash dashboard prototype with statistical analysis capabilities tailored for tribal lands, enhancing safety data utilization.
Findings
Tribal land roads, especially rural ones, are more dangerous.
The dashboard effectively locates high-risk tribal areas.
Crash analysis reveals key causes and patterns.
Abstract
Tribal lands in the United States have consistently exhibited higher crash rates and injury severities compared to other regions. To address this issue, effective data-driven safety analysis methods are essential for resource allocation and tribal safety program development. This study outlines the minimum data requirements and presents a generic tribal crash dashboard prototype to enable feasible and reproducible tribal crash analysis. The dashboard offers statistical performance measurement techniques, tracks tribal safety trends using various indicators, and updates in with incoming data, making it adaptable for any state. Specifically, the dashboard's capabilities are showcased using Wisconsin tribal crash data, locating high-risk tribal land areas while analyzing and comparing statewide crashes and tribal crashes based on severity. The results reveal that tribal land roads,…
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Taxonomy
TopicsAgriculture and Farm Safety · Occupational Health and Safety Research · Traffic and Road Safety
