A recurrent neural network model for predicting two-leader car-following behavior

• Published in: Transportation letters Vol. ahead-of-print; no. ahead-of-print; pp. 1 - 15
• Main Authors: Das, Sanhita, Maurya, Akhilesh Kumar, Dey, Arka
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 27.05.2024
• Subjects: Car-following; disordered traffic; mixed traffic; neural network; two-leader
• ISSN: 1942-7867; 1942-7875
• DOI: 10.1080/19427867.2023.2205190

Unlike lane-based traffic where each driver has a distinct leader, the subject driver in disorderly traffic may interact with multiple vehicles in-front. The existence of lateral interactions among the vehicles in-front adds even more complexity to modeling the human-driving process. Utilizing trajectory data extracted from an instrumented vehicle study, this research attempts to propose a gated recurrent unit neural network model to predict responses of vehicles interacting with two leading vehicles simultaneously. The recurrent neural network model can illustrate realistic human-like following behavior of drivers, much better than the classical optimal velocity-based models in terms of trajectory reproducing accuracy. The model can also explain the closing-in, shying-away behavior and local stability properties. Results of the study provide insights into the driving behavioral phenomena of disorderly traffic flows and can contribute to the development of a realistic microsimulation model, smarter autonomous systems, and in-traffic safety evaluation as well.