Toward a Traffic World Model: Learning the Physics of Signalized Roads with Guided Diffusion

Traffic engineers describe how vehicles move along a road with physics-based models built from a few equations. These models are compact and interpretable, but each fits one kind of measurement and must be calibrated by optimization before use. We ask whether a generative model trained only on traffic states and the corresponding signal timing can learn the physics of a signalized road, and whether it can serve as a world model that answers questions from partial sensor data. We present DiffLink, a diffusion model over the time-space diagram of a signalized link. The state is a two-channel image of density and flow, the signal is the action, and no traffic equation enters training. On SUMO data, states generated from the signal alone reproduce queue formation and discharge. Their mean arrival rate, delay, and maximum queue length match the test set within 4%, and their fundamental diagram, discharge flow, and queue-tail growth rate match the simulator’s. When the green is shortened, the model’s queue grows by 4.9 m against the simulator’s 5.1 m, at a correlation of 0.93 across the 32 simulations. Because the model is a prior over full states, any sensor becomes a query at sampling time, with no retraining. Guided by two stop-bar detectors, DiffLink estimates the arrival rate as well as a vehicle count does, and it reconstructs the maximum queue length per window with a correlation of 0.74, against 0.51 for deterministic queuing from the same counts. We close with what still separates DiffLink from a full traffic world model.