Citation

Cui J, Yu G, Zhou B, Liu Q, Guan Z. J. Transp. Eng. A: Systems 2021; 147(12): e04021092.

Copyright

(Copyright © 2021, American Society of Civil Engineers)

DOI

10.1061/JTEPBS.0000598

PMID

unavailable

Abstract

Lane changing is a fundamental driving task and is closely related to traffic operation. The safety performance of vehicle driving and traffic flow is supposed to be substantially improved if lane-changing behavior can be precisely predicted. To this end, a model based on the Gated Recurrent Unit (GRU) is proposed in this study for freeway on-ramp lane-changing behavior forecasting. One specific feature of the model is that it enables the filtering out of the lateral oscillation behavior and helps enhance forecast accuracy. The experiment results show that the model achieves an accuracy of 96.85% for lane-changing behavior forecasting, and outperforms the GRU model without lateral acceleration input and the LSTM model by 5.12% and 4.51%, respectively.

Language: en

Keywords

Decision-making forecast; Freeway on-ramp; Gated recurrent unit (GRU); Lane changing; Lateral oscillation