A systematic review of machine learning-based microscopic traffic flow models and simulations

Abstract

Microscopic traffic flow models and simulations are crucial for capturing vehicle interactions and analyzing traffic. They can provide critical insights for transport planning, management, and operation through scenario testing and optimization. With the growing availability of high-resolution data and rapid advancements in machine learning (ML) techniques, ML-based microscopic traffic flow models are emerging as promising alternatives to traditional physical models, offering improved accuracy and greater flexibility. Although many models have been developed, comprehensive studies that critically assess the strengths and weaknesses of these models and the overall ML-based approach are lacking. To fill this gap, this study presents a systematic review of ML-based microscopic traffic flow models and simulations, covering both car-following and lane-changing behaviors. This review identifies key areas for future research, including the development of methods to improve model transferability across different operational design domains, the need to capture both driver-specific and location-specific heterogeneity via benchmark datasets, and the incorporation of advanced ML techniques such as meta-learning, federated learning, and causal learning. Additionally, enhancing model interpretability, accounting for mesoscopic and macroscopic traffic impacts, incorporating physical constraints in model training, and developing ML models designed for autonomous vehicles are crucial for the practical adoption of ML-based microscopic models in traffic simulations.