Owen Matejka

Date of Award

6-18-2025

Document Type

Thesis

Publisher

Santa Clara : Santa Clara University, 2025

Department

Computer Science and Engineering

First Advisor

Yuhong Liu

Abstract

Traffic intersections represent critical points of conflict in urban transportation networks, with over 40,000 traffic-related fatalities occurring annually in the United States alone. Traditional intersection monitoring systems, based on timer controls and inductive loop detectors, lack the sophisticated detection capabilities needed to address modern traffic safety challenges. This thesis presents the Traffic Control Risk Analysis System (TCRAS), a low-cost, computer vision-based solution that democratizes access to advanced intersection monitoring capabilities. TCRAS leverages edge AI processing through Hailo neural network accelerators combined with open-source computer vision algorithms to provide comprehensive intersection analysis. The system performs real-time multi-class object detection and tracking of vehicles, pedestrians, and traffic lights, enabling sophisticated violation detection including red light violations, yellow light violations, jaywalking, congestion monitoring, and intersection blocking. The TCRAS Index provides a standardized 0-100 safety metric that synthesizes multiple violation types and efficiency factors into a single comparable score. The system was deployed to a real intersection for monitoring in Santa Clara, to prove its feasibility. This work demonstrates that sophisticated traffic analysis capabilities can be achieved without prohibitive infrastructure investments, potentially accelerating the adoption of vision-based safety systems in support of Vision Zero initiatives.

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