Speaking

This study leveraged data science and GIS for the West Virginia state-wide vulnerable road user (VRU) assessment to enhance road safety for everyone. Recognizing the critical need for accurate data and tools to identify high-risk areas and devise effective interventions, our multifaceted approach led to the development of a comprehensive VRU risk model, integrating various data sources. Through statistical analysis, we identified key predictors of VRU crashes, resulting in a weighted risk-scoring system for transportation network segments. This proactive, data-driven systemic analysis enables decision-makers to implement safety measures before crashes occur. Our presentation will highlight the application of GIS and data science in pinpointing high-risk areas and shaping strategies to protect VRUs in West Virginia.