TrafficFlowNet: A Neural Transport Model for Dynamic Traffic Flows.

Abstract

Traffic congestion remains a persistent barrier to mobility and efficiency, especially in developing regions with limited infrastructure. Addressing this challenge requires robust traffic flow modeling, which remains challenging due to nonlinear spatiotemporal dependencies and external perturbations. Current modeling approaches primarily characterize temporal fluctuations at the regional scale, without a comprehensive formulation of the governing dynamics. Here, we present TrafficFlowNet, a physics-informed deep learning model that formulates traffic flow as a transport process across interconnected road nodes. By embedding the transport dynamics within a graph neural network (GNN) backbone, TrafficFlowNet models directional flows driven by traffic-volume gradients. We further incorporate total variation (TV) regularization into a min-max optimization framework to address the oversmoothing issue common in GNNs and adopt a generative adversarial structure to capture realistic dynamics. In experiment, TrafficFlowNet outperformed sixteen state-of-the-art methods across real-world highway traffic sensor benchmarks (PEMS03, PEMS04, PEMS07, and PEMS08, each including >150 sensors, >15 000 time steps). On PEMS03 data, TrafficFlowNet achieves a mean absolute error (MAE) of 13.314, representing up to 8.43% improvements over prior best-performing model (14.541). These results highlight the potential and foundational insight of our physics-grounded framework for traffic prediction.