Toward Data-Driven Simulation of Network-Wide Traffic: A Multi-Agent Imitation Learning Approach Using Urban Vehicle Trajectory Data

This paper aims to model network-wide vehicle movements and traffic state evolution by considering the interactions among vehicles and link traffic conditions through a multi-agent imitation learning framework. The goal is to propose a framework for data-driven simulation of network-wide traffic by directly learning traffic behaviours from observed vehicle trajectory data. We thus develop a data-driven simulation model that applies the existing Multi-Agent Generative Adversarial Imitation Learning (MAGAIL) framework considering both Vehicle and Link agents to learn vehicles' link-to-link transitions and within-link movements (travel speed), and road links' traffic state evolution patterns simultaneously, which is referred to as MAGAIL-VL model. We evaluate the model's performance in terms of its ability to generate realistic vehicle trajectories, route distribution, and link travel time and congestion state. The comparison with several deep-learning based benchmark models shows that vehicle and link agents in the developed MAGAIL-VL model can mimic real-world vehicle movement and link state change patterns, producing superior performance over other tested methods.