SpinFlow: A Physics-Informed Spin Field Framework for Traffic Phase Inference and Transition Detection
Active traffic management (ATM) is frequently hindered by traditional macroscopic models and rigid empirical thresholds that fail to capture metastable phase precursors, resulting in delayed, reactive interventions. To address this, we propose SpinFlow, a physics-informed spin-field framework unifying Kerner's three-phase theory with statistical physics for continuous macroscopic traffic phase inference. Inspired by the Heisenberg model, SpinFlow parametrizes spatially varying phase weights via a latent spin vector and a competitive-equilibrium mapping, allowing synchronized flow to emerge naturally. A physics-regularized Expectation-Maximization algorithm inverts this latent structure fr...
| Authors | Haopeng Deng, Fucheng Zheng, Xinhai Xia |
|---|---|
| Primary category | physics.soc-ph |
| Published | 22 May 2026 |
| Last updated at source | 22 May 2026 |
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