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A Car-Following Model considering the Effect of Following Vehicles under the Framework of Physics-Informed Deep Learning.
- Source :
-
Journal of Advanced Transportation . 8/31/2022, p1-12. 12p. - Publication Year :
- 2022
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01976729
- Database :
- Academic Search Index
- Journal :
- Journal of Advanced Transportation
- Publication Type :
- Academic Journal
- Accession number :
- 158815755
- Full Text :
- https://doi.org/10.1155/2022/3398862