A Review of Connected and Automated Vehicle Traffic Flow Models for Next-Generation Intelligent Transportation Systems

Abstract

Connected and Automated Vehicle (CAV) technology is a rapidly developing field that is expected to transform the transportation industry. This study provides an overview of traffic flow models for Connected and Automated Vehicles (CAVs). The study explores the different levels of automation in CAVs and discuss the strengths and limitations of three categories of traffic flow models: microscopic, mesoscopic, and macroscopic. The article highlights that while microscopic models provide a high level of detail and accuracy, they require significant data input and computational resources, making them difficult to scale up to large networks or regions. Mesoscopic models are more computationally efficient but still provide useful detail and can simulate traffic flow over a larger area than microscopic models. Macroscopic models, while most computationally efficient, may not capture the effects of specific traffic management strategies or provide the level of detail necessary to capture individual vehicle movements and driver behaviors. The study emphasizes the need to take into account other factors that can influence CAV traffic flow, such as human-driven vehicles, road infrastructure, and communication protocols. By providing insights into the strengths and weaknesses of each approach, this article aims to facilitate the development of next-generation Intelligent Transportation Systems (ITS) that effectively manage traffic flow and fully realize the potential of CAVs.