Engaging with Industry Experts at the Autonomous Trucking in North Dakota Conference

I had an enriching experience attending the Autonomous Trucking in North Dakota Conference, where I engaged with industry professionals, researchers, and policymakers to discuss the future of autonomous transportation. The conference featured insightful sessions on autonomous trucking, infrastructure planning, cybersecurity, and agricultural logistics, providing a comprehensive understanding of the opportunities and challenges in this evolving field. I also participated in discussions on safety planning, regulatory considerations, and the implementation of autonomous trucking in rural areas, contributing my perspectives on Connected and Autonomous Vehicles (CAVs) and their impact on transportation systems. This event was an excellent platform for networking, exchanging ideas, and gaining valuable insights that will support my ongoing research and academic endeavors.

Predictive and Spatial Analysis of Road Traffic Crashes: A Focus on Young Drivers in North Dakota

This study examines traffic crashes involving young drivers in North Dakota, analyzing both spatial patterns and predictive factors that influence crash severity. By using Geographic Information Systems (GIS) techniques and logistic regression modeling, the research identifies high-risk locations and key crash contributors over a ten-year period (2013–2023).

The findings show that speed limits play a critical role in crash severity, with higher speeds significantly increasing the likelihood of severe outcomes. Surprisingly, clear weather conditions were also associated with more severe crashes, suggesting that drivers may take greater risks in favorable driving conditions. The study’s GIS-based analysis maps persistent crash hotspots across the state, helping transportation planners focus on the most dangerous areas.

By combining data-driven insights with spatial analysis, this research provides actionable recommendations, such as enhanced speed enforcement, improved road safety infrastructure, and targeted public awareness campaigns for young drivers. These findings contribute to a more strategic approach to improving traffic safety and reducing serious crashes on North Dakota’s roads.

Showcasing My Research at GIS Day

Presented my research project, Spatial and Temporal Hot Spot Analysis of Traffic Crashes in North Dakota (2013–2023), at GIS Day. The study utilizes GIS techniques to identify crash hotspots, analyze temporal trends, and provide data-driven insights for traffic safety improvements.

Spatial and Temporal Hot Spot Analysis of Traffic Crashes in North Dakota (2013–2023)

This poster presents a GIS-based analysis of traffic crash hotspots in North Dakota from 2013 to 2023. The study utilizes Hot Spot Analysis, Kernel Density Estimation, and Emerging Hot Spot Analysis to identify high-risk crash locations and trends over time. Key findings highlight high-crash corridors, contributing factors such as speed and road conditions, and demographic insights related to crash severity. The research aims to provide data-driven recommendations for improving traffic safety and guiding infrastructure planning.