Traffic Adaptive Moving-window Service Patrolling for Real-time Incident Management during High-impact Events
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912334726823936 |
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| author | Lei, Haozhe Yang, Ya-Ting Li, Tao Bian, Zilin Zuo, Fan Rangan, Sundeep Ozbay, Kaan |
| author_facet | Lei, Haozhe Yang, Ya-Ting Li, Tao Bian, Zilin Zuo, Fan Rangan, Sundeep Ozbay, Kaan |
| contents | This paper presents the Traffic Adaptive Moving-window Patrolling Algorithm (TAMPA), designed to improve real-time incident management during major events like sports tournaments and concerts. Such events significantly stress transportation networks, requiring efficient and adaptive patrol solutions. TAMPA integrates predictive traffic modeling and real-time complaint estimation, dynamically optimizing patrol deployment. Using dynamic programming, the algorithm continuously adjusts patrol strategies within short planning windows, effectively balancing immediate response and efficient routing. Leveraging the Dvoretzky-Kiefer-Wolfowitz inequality, TAMPA detects significant shifts in complaint patterns, triggering proactive adjustments in patrol routes. Theoretical analyses ensure performance remains closely aligned with optimal solutions. Simulation results from an urban traffic network demonstrate TAMPA's superior performance, showing improvements of approximately 87.5\% over stationary methods and 114.2\% over random strategies. Future work includes enhancing adaptability and incorporating digital twin technology for improved predictive accuracy, particularly relevant for events like the 2026 FIFA World Cup at MetLife Stadium. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_11570 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Traffic Adaptive Moving-window Service Patrolling for Real-time Incident Management during High-impact Events Lei, Haozhe Yang, Ya-Ting Li, Tao Bian, Zilin Zuo, Fan Rangan, Sundeep Ozbay, Kaan Optimization and Control Machine Learning This paper presents the Traffic Adaptive Moving-window Patrolling Algorithm (TAMPA), designed to improve real-time incident management during major events like sports tournaments and concerts. Such events significantly stress transportation networks, requiring efficient and adaptive patrol solutions. TAMPA integrates predictive traffic modeling and real-time complaint estimation, dynamically optimizing patrol deployment. Using dynamic programming, the algorithm continuously adjusts patrol strategies within short planning windows, effectively balancing immediate response and efficient routing. Leveraging the Dvoretzky-Kiefer-Wolfowitz inequality, TAMPA detects significant shifts in complaint patterns, triggering proactive adjustments in patrol routes. Theoretical analyses ensure performance remains closely aligned with optimal solutions. Simulation results from an urban traffic network demonstrate TAMPA's superior performance, showing improvements of approximately 87.5\% over stationary methods and 114.2\% over random strategies. Future work includes enhancing adaptability and incorporating digital twin technology for improved predictive accuracy, particularly relevant for events like the 2026 FIFA World Cup at MetLife Stadium. |
| title | Traffic Adaptive Moving-window Service Patrolling for Real-time Incident Management during High-impact Events |
| topic | Optimization and Control Machine Learning |
| url | https://arxiv.org/abs/2504.11570 |