动态调整巡逻路线，大幅提升重大活动期间应急响应效率
Traffic Adaptive Moving-window Service Patrolling for Real-time Incident Management during High-impact Events
- 基于交通预测与实时投诉分析，动态优化巡逻路径
- 相比固定巡逻提升87.5%，随机巡逻提升114.2%的响应效率
- 适合城市交通管理、大型活动保障等场景应用
本文提出交通自适应移动窗口巡逻算法（TAMPA），用于改善体育赛事和演唱会等重大活动期间的实时事故管理。此类活动对交通网络造成巨大压力，需高效且可适应的巡逻方案。TAMPA融合预测交通建模与实时投诉估计，通过动态规划在短周期内持续调整巡逻策略，有效平衡即时响应与路径效率。利用Dvoretzky-Kiefer-Wolfowitz不等式检测投诉模式显著变化，触发巡逻路线的主动调整。理论分析表明其性能接近最优解。基于城市交通网络的仿真结果表明，TAMPA相较静态方法提升约87.5%，相较随机策略提升114.2%。未来工作将增强自适应能力，并引入数字孪生技术以提高预测精度，尤其适用于2026年FIFA世界杯在梅特利夫体育场举办的情况。
原文摘要 · Abstract (English)
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.
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