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Tribal lands in the United States have consistently exhibited higher crashrates and injury severities compared to other regions. To address this issue,effective data-driven safety analysis methods are essential for resourceallocation and tribal safety program development. This study outlines theminimum data requirements and presents a generic tribal crash dashboardprototype to enable feasible and reproducible tribal crash analysis. Thedashboard offers statistical performance measurement techniques, tracks tribalsafety trends using various indicators, and updates in with incoming data,making it adaptable for any state. Specifically, the dashboard's capabilitiesare showcased using Wisconsin tribal crash data, locating high-risk tribal landareas while analyzing and comparing statewide crashes and tribal crashes basedon severity. The results reveal that tribal land roads, particularly ruralones, are more dangerous than those in other Wisconsin areas. The dashboardalso examines crash types to uncover the causes and insights behind Wisconsintribal crashes. Demonstrating this dashboard concept reveals its potential tofacilitate a more intuitive understanding of tribal crashes from a statisticalstandpoint, thereby enabling more effective applications of available datasources and the development of targeted safety countermeasures.

Developing a Conceptual Tribal Crash Safety Dashboard: Data-Driven Strategies for Identifying High-Risk Areas and Enhancing Tribal Safety Programs