TY  - JOUR
PY  - 2021//
TI  - Forecasting freeway on-ramp lane-changing behavior based on GRU
JO  - Journal of transportation engineering, Part A: Systems
A1  - Cui, Jieming
A1  - Yu, Guizhen
A1  - Zhou, Bin
A1  - Liu, Qiujun
A1  - Guan, Zhengguo
SP  - e04021092
EP  - e04021092
VL  - 147
IS  - 12
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 2473-2907
UR  - http://dx.doi.org/10.1061/JTEPBS.0000598
ID  - ref1
ER  -