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Enhancing Intersection Safety through Advanced Planning and AI Integration

This research develops innovative methods for improving intersection safety through three integrated approaches: optimized intersection planning, connected and automated vehicle (CAV) integration, and artificial intelligence (AI)-driven pedestrian safety enhancement. The project addresses the safe accommodation of diverse users including private vehicles, trucks, transit, and pedestrians while incorporating emerging technologies such as sensors, control systems, and CAVs. Using population-based metaheuristic algorithms and VISSIM microsimulation modeling, the research will optimize intersection development through cost minimization that includes construction, maintenance, user costs, delays, accidents, and emissions. The CAV integration component focuses on infrastructure readiness for varying levels of vehicle autonomy through simulation and analysis models, cooperative perception systems, and Vehicle-to-Everything communication. The pedestrian safety advancement leverages multi-modal RGBT sensor data, SAM2 AI tracking models, LiDAR integration, and surrogate safety measures to create predictive safety systems. The methodology builds on extensive University of Maryland experience in transportation network optimization and incorporates real-world sensor deployments at Maryland sites with over 25 hours of interaction data collection and analysis.