用物理约束算法发现极端天气后交通系统隐藏损伤，避免误判恢复假象。
Unlocking the Invisible Urban Traffic Dynamics under Extreme Weather: A New Physics-Constrained Hamiltonian Learning Algorithm
- 结合物理约束与哈密顿结构识别，挖掘交通系统的隐藏动态变化。
- 伦敦暴雨事件中检测到64.8%的结构性损伤，传统指标完全未察觉。
- 适合城市规划与交通韧性评估人员，用于真实健康状态判断。
城市交通系统在极端天气下面临日益严峻的韧性挑战，但现有评估方法依赖表面恢复指标，难以发现隐藏的结构性损伤。现有方法无法区分真正的系统恢复与‘虚假恢复’——即交通指标看似恢复正常，但底层动力学已永久退化。为此，本文提出一种融合‘结构不可逆性检测’与‘能量景观重构’的物理约束哈密顿学习算法。该方法提取低维状态表征，通过物理约束优化识别准哈密顿结构，并基于能量景观对比量化结构变化。对2021年伦敦极端降雨事件的分析表明，尽管表面指标已完全恢复，本算法仍检测出64.8%的结构性损伤，远超传统监测手段。该框架为基础设施的主动风险评估提供支持，使投资决策基于真实系统健康状态，而非误导性的表面指标。
原文摘要 · Abstract (English)
Urban transportation systems face increasing resilience challenges from extreme weather events, but current assessment methods rely on surface-level recovery indicators that miss hidden structural damage. Existing approaches cannot distinguish between true recovery and "false recovery," where traffic metrics normalize, but the underlying system dynamics permanently degrade. To address this, a new physics-constrained Hamiltonian learning algorithm combining "structural irreversibility detection" and "energy landscape reconstruction" has been developed. Our approach extracts low-dimensional state representations, identifies quasi-Hamiltonian structures through physics-constrained optimization, and quantifies structural changes via energy landscape comparison. Analysis of London's extreme rainfall in 2021 demonstrates that while surface indicators were fully recovered, our algorithm detected 64.8\% structural damage missed by traditional monitoring. Our framework provides tools for proactive structural risk assessment, enabling infrastructure investments based on true system health rather than misleading surface metrics.
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