Traffic Adaptive Moving-window Service Patrolling for Real-time Incident Management during High-impact Events

2025-04-15

AI Summary

This paper addresses the problem of delayed and inefficient traffic patrol responses during large-scale, high-impact events (e.g., sports games, concerts). To this end, we propose a real-time adaptive patrol framework. Methodologically, it integrates DKW inequality–driven complaint anomaly detection with dynamic programming optimization over a rolling time window, enabling proactive patrol deployment guided by predictive traffic modeling and real-time complaint estimation. Our key contributions are twofold: (i) the first application of the DKW statistical bound to dynamic complaint anomaly identification, and (ii) the design of a rolling optimization paradigm with theoretical near-optimality guarantees, shifting patrol strategies from reactive to proactive. Simulation results demonstrate that our approach improves average response efficiency by 87.5% and 114.2% over static and random baselines, respectively.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

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.

Problem

• Improving real-time incident management during major events
• Optimizing patrol deployment with predictive traffic modeling
• Balancing immediate response and efficient routing dynamically

Innovation

• Dynamic programming optimizes patrol strategies adaptively
• Predictive traffic modeling integrates real-time complaint estimation
• Dvoretzky-Kiefer-Wolfowitz detects shifts for proactive adjustments