Date of Award

May 2024

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Geography & Geographic Information Science

First Advisor

Enru Wang

Abstract

Vehicle crashes represent a critical global issue, resulting in numerous fatalities, injuries, and significant economic and societal impacts. In response, government officials worldwide are developing strategies and policies to enhance road safety and promote sustainable transportation systems, aiming for zero fatalities and injuries. Understanding the causes of crashes is essential for these efforts. This study focuses on identifying spatial and temporal patterns and contributing factors for the crash severity of vehicle crashes in Minnesota. Spatial distribution of crash hotspots is identified and visualized using spatial autocorrelation (global Moran I), hotspot analysis (Getis–Ord Gi*), and kernel density estimation in ArcGIS Pro. The analysis reveals crash hotspots near metropolitan areas like the Twin Cities and Duluth. Ordinal logistic regression analysis identifies important factors that significantly contribute to crash severity, including impaired driving due to alcohol or drugs, driving with physical disabilities, clear weather conditions, and exceeding speed limits. The study’s findings contribute to building a reliable crash model to mitigate negative socio-economic impacts of fatal crashes in Minnesota, creating safer road environments.

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