Projects

Bridge strikes caused by insufficient vertical clearance remain a persistent safety and infrastructure challenge across the U.S. This project develops an AI-powered mobile LiDAR system to automatically detect, assess, and visualize bridge clearances and vertical obstructions, enabling DOTs to proactively reduce collision risk.

This project evaluates the use of connected passenger vehicle sensor data to obtain reliable, real-time pavement friction measurements. By validating vehicle-based friction estimates against traditional testing methods, the research will support improved roadway safety and pavement management decisions.

This project examines how double parking affects traffic safety and congestion in urban environments. By combining field observations and behavioral modeling, the research will support data-driven curb management policies that reduce unsafe parking behaviors.

This project examines how public transit system characteristics are associated with roadway safety outcomes across metropolitan areas in the United States, with a focus on New England. Using data-driven modeling, the research will identify which transit features are most strongly linked to crash risk reduction.

This project develops and evaluates practical intervention strategies to increase seatbelt use among motorcoach passengers through targeted training and promotional actions. By equipping carriers, terminal operators, and regulators with low-cost tools, the research aims to improve passenger safety and compliance with existing seatbelt requirements.

This project develops a network-level, data-driven approach to identify and prioritize locations for High Friction Surface Treatment using remote sensing and roadway performance data. By integrating geometric, surface, and crash characteristics of horizontal curves, the research will support proactive safety investments and improved crash prevention.

This project evaluates how different Transit Signal Priority signal display designs influence crash risk and driver behavior at signalized intersections. By analyzing crash data across a national set of TSP installations, the research will identify signal design characteristics associated with safer outcomes and inform future guidance.

This project applies artificial intelligence and network analysis to support risk-informed decision-making for culvert infrastructure maintenance. By predicting culvert condition and identifying assets critical to network connectivity, the research will help agencies prioritize inspections and investments to reduce failure risks.

This project examines how Advanced Driver Assistance Systems influence drivers’ hazard anticipation behaviors and whether automation alters how drivers detect and respond to roadway risks. Using simulator-based experiments and behavioral analysis, the research will inform training strategies to support safe driver supervision in increasingly automated vehicles.