A Physics-Informed Deep Learning Paradigm for Car-Following Models
Video
Team Information
Team Members
Zhaobin Mo, PhD Student, Department of Civil Engineering and Engineering Mechanics, Columbia Engineering
Faculty Advisor: Xuan (Sharon) Di, Associate Professor at Civil Engineering and Engineering Mechanics, Columbia Engineering
Abstract
Car-following behavior has been extensively studied using physics-based models, such as Intelligent Driving Model (IDM). These models successfully interpret traffic phenomena observed in the real world but may not fully capture the complex cognitive process of driving. Deep learning models, on the other hand, have demonstrated their power in capturing observed traffic phenomena but require a large amount of driving data to train. This paper aims to develop a family of neural network based car-following models that are informed by physics-based models, which leverage the advantage of both physics-based (being data-efficient and interpretable) and deep learning based (being generalizable) models.
We demonstrate the superior performance of PIDL with the Next Generation SIMulation (NGSIM) dataset over baselines, especially when the training data is sparse. The results demonstrate the superior performance of neural networks informed by physics over those without.
Team Lead Contact
Zhaobin Mo: zm2302@columbia.edu
