Unlocking the Invisible Urban Traffic Dynamics under Extreme Weather: A New Physics-Constrained Hamiltonian Learning Algorithm

TL;DR

This paper introduces a physics-constrained Hamiltonian learning algorithm that detects hidden structural damage in urban traffic systems under extreme weather, surpassing traditional surface-level recovery metrics.

Contribution

The paper presents a novel physics-constrained Hamiltonian learning method combining structural irreversibility detection and energy landscape reconstruction for urban traffic analysis.

Findings

Detected 64.8% structural damage missed by traditional metrics

Successfully distinguished true recovery from false recovery in traffic systems

Provided a proactive risk assessment framework for infrastructure investments

Abstract

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.