Traffic Model

Traffic modeling aims to create accurate representations of vehicular movement to optimize transportation systems and improve safety. Current research emphasizes data-driven approaches, employing deep learning architectures like transformers and neural networks (including physics-informed and generative adversarial networks) to capture complex spatiotemporal dynamics and driver behavior, often incorporating macroscopic and microscopic models. These advancements are crucial for applications such as autonomous vehicle development, traffic control optimization, and the development of more realistic and controllable driving simulators, ultimately leading to improved efficiency and safety in transportation networks.