Graph Attention Network for Lane-Wise and Topology-Invariant Intersection Traffic Simulation

Abstract

Traffic congestion poses significant economic, environmental, and social challenges. High-resolution loop detector data and signal state records from Automated Traffic Signal Performance Measures (ATSPM) offer new opportunities for traffic signal optimization at intersections. However, additional factors such as geometry, traffic volumes, Turning-Movement Counts (TMCs), and human driving behaviors complicate this task. Existing simulators (e.g., SUMO, Vissim) are computationally intensive, while machine learning models often lack lane-specific traffic flow estimation. To address these issues, we propose two computationally efficient Attentional Graph Auto-Encoder frameworks as “Digital Twins” for urban traffic intersections. Leveraging graph representations and Graph Attention Networks (GAT), our models capture lane-level traffic flow dynamics at entry and exit points while remaining agnostic to intersection topology and lane configurations. Trained on over 40,000 hours of realistic traffic simulations with affordable GPU parallelization, our framework produces fine-grained traffic flow time series. This output supports critical applications such as estimating Measures of Effectiveness (MOEs), scaling to urban freeway corridors, and integrating with signal optimization frameworks for improved traffic management.