A Configurable Penalty System for Transportation Management: A University Campus Case Study

Effective traffic management systems rely on a balance between penalties for violations and rewards for compliance, fostering a culture of responsibility and cooperation. In this paper, we present a configurable penalty system designed to address the unique traffic management challenges faced by university campuses. Our approach integrates advanced AI models, including YOLO-based object detection, to monitor and evaluate compliance with campus-specific traffic rules. The system dynamically assigns rewards and penalties based on real-time and historical data, promoting adherence while maintaining a supportive academic environment. To validate our system, we utilized real-world data from the University of Isfahan, demonstrating its effectiveness and adaptability. Furthermore, the system’s modular design allows for customization to broader contexts, such as public road traffic management.

کلیدواژه ها:

Configurable Penalty and Reward ، Penalty System ، Transportation Management ، Traffic Rules