SpinFlow parametrizes traffic phases with a latent spin vector and competitive-equilibrium mapping, then uses physics-regularized EM to invert the field from trajectories and localize transitions via a new PED metric.
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A saturation-based Optimal Velocity Model is introduced that enforces bounded acceleration, preserves long-wave instability for stop-and-go waves, and modifies the stability threshold compared to the classical OVM.
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SpinFlow: A Physics-Informed Spin Field Framework for Traffic Phase Inference and Transition Detection
SpinFlow parametrizes traffic phases with a latent spin vector and competitive-equilibrium mapping, then uses physics-regularized EM to invert the field from trajectories and localize transitions via a new PED metric.
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A Saturation-Based Optimal Velocity Model for Traffic Flow Dynamics
A saturation-based Optimal Velocity Model is introduced that enforces bounded acceleration, preserves long-wave instability for stop-and-go waves, and modifies the stability threshold compared to the classical OVM.