Learning Traffic Flow Dynamics with Stochastic Physics-Informed Neural Cellular Automata
The research developed a Physics-Informed Neural Cellular Automaton (PI-NCA) to learn traffic flow dynamics from data while enforcing physical constraints, such as vehicle conservation. This model outperforms standard NCA by predicting vehicle velocities and explicitly updating positions. The stochastic version captures traffic randomness effectively, showing superior accuracy across various complex traffic models.