A Car-Following Model considering the Effect of Following Vehicles under the Framework of Physics-Informed Deep Learning. (31st August 2022)

• Record Type: Journal Article
• Authors: Xu, Le; Ma, Jianxiao; Wang, Yuchen
• Other Names: Yang Yang Academic Editor.
• Is Part Of: Journal of advanced transportation. Volume 2022(2022)
• Publication Date: 2022-08-31
• Subjects: Transportation -- Periodicals; 388.05
• DOI: 10.1155/2022/3398862
• Languages: English
• ISSNs: 0197-6729

Abstract

Abstract : Car-following models have been studied for a long time, and many traffic engineers and researchers have devoted attention to them. With the increase in machine learning, this paper proposes a fusion model based on the physics-informed deep learning framework. The purpose of this paper is to inherit the predecessors' ideas, transform them to fit a new framework, and improve the framework's accuracy. The IDM-D (intelligent driver model development) involves reenabling the effect of the following vehicle to form a complementary model (not car-following model) with the IDM (intelligent driver model). The pretreated NGSIM data are used for calibration and validation. The IDM and the IDM-D are combined with the LSTM under the framework of physics-informed deep learning, and the results are mixed in a ratio to form the final result. Using test data for simulation, the results reveal that the IDM-informed LSTM shows better performance than the LSTM and that the fusion model further improves the MSE (mean square error) of the IDM-informed LSTM. The fusion increases the accuracy during the deceleration process, which is better than just a single IDM-informed LSTM. The fusion model further explains drivers' deceleration behaviors.