Citation

Liu J, Jiang R, Zhao J, Shen W. Transp. Res. C Emerg. Technol. 2023; 154: e104275.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.trc.2023.104275

PMID

unavailable

Abstract

Car-following models have been extensively studied using either theory-driven models or data-driven models. On the other hand, theory-driven models have low accuracy, whereas data-driven models have poor interpretability and require a high-quality training dataset. Furthermore, the majority of studies focused on predicting short-term trajectory based on a single vehicle. This paper aims to combine physics knowledge with deep learning and stochasticity to completely capture the genuine regularities of car-following behaviors, leveraging the benefits of both physics-based (interpretability) and deep learning-based (data-efficient and generalizability) models. We design a quantile-regression physics-informed deep learning car-following model (QRPIDL-CF). The physics-informed computational graph is integrated with a long short-term memory (LSTM) architecture (abbreviated as PIDL). By incorporating the quantile regression to the loss function of the PIDL model, stochastic behaviors can be produced without prior assumptions of drivers. To validate the proposed model, data from a 25-vehicle platoon experiment was used. The prediction results prove that given the first vehicle's trajectory and other vehicles' initial states, it can preserve the high precision in learning car-following behaviors and introduce small disturbances into car-following in a vehicle-platoon just the same as the real driving process. The trajectory prediction of the whole platoon shows that the proposed hybrid QRPIDL-CF model can better perform the macro-traffic behavior of car-following than every single model from the hybrid QRPIDL-CF model.

Language: en

Keywords

2D-IDM model; Car-following models; LSTM; Physics-informed deep learning (PIDL); Quantile-regression (QR)