Microscopic Modelling of Car-Following Behaviour: Developments and Future Directions

Abstract

Review Microscopic Modelling of Car-Following Behaviour: Developments and Future Directions Yinglong He 1, * , Quan Zhou 2, * , Chongming Wang 3, Ji Li 2, Bin Shuai 2, Lei Lei 4, and Hongming Xu 2 1 School of Mechanical Engineering Sciences, University of Surrey, Guildford GU2 7XH, UK 2 Department of Mechanical Engineering, University of Birmingham, Birmingham B15 2TT, UK 3 Institutes for Future Transport and Cities, Coventry University, Coventry CV1 5FB, UK 4 College of Information Science and Engineering, Jiaxing University, Jiaxing 314001, China * Correspondence: yinglong.he@surrey.ac.uk (Y.H.); q.zhou@bham.ac.uk (Q.Z.) Received: 17 April 2023 Accepted: 21 June 2023 Published: 27 June 2023 Abstract: The study of driving behaviour has become increasingly important in the development of transport and vehicle technologies. Microscopic traffic models simulate individual driver behaviour to understand and predict traffic flow. One of the key components in microscopic simulation is the car-following (CF) model, which describes the behaviour of vehicles in terms of how they follow the vehicle in front of them. Some excellent reviews of CF models are available, however, to the best of the authors’ knowledge, none of them provides a comprehensive analysis that covers and compares different model categories including kinematics-based, dynamics-based, psychological-based, and learning-based. This paper, therefore, provides an overview of the developments and future directions of CF models, encompassing all the previously mentioned categories. It first introduces the fundamental concepts of traffic models, in particular CF models. It then reviews the progress of CF models, which are classified into the above four categories. The advantages and limitations of existing CF models are discussed. The paper further identifies several research directions for future work, including the integration of emerging vehicle technologies, the incorporation of real-world traffic data, and the calibration and validation of model parameters. It concludes by emphasizing the importance of interdisciplinary collaboration and the need for further research to improve the accuracy and practicality of CF models.