中圖分類號： TP181 文獻(xiàn)標(biāo)識碼： A DOI： 10.19358/j.issn.2096-5133.2022.03.010 引用格式： 王嘉偉,，陳鋒. 基于雙向GRU與殘差擬合的車輛跟馳建模[J].信息技術(shù)與網(wǎng)絡(luò)安全,，2022,，41(3)：59-64.
A car-following model based on bidirectional GRU and residual fitting
Wang Jiawei
(School of Information Science and Technology，University of Science and Technology of China,，Hefei 230002,，China)
Abstract： In order to improve the accuracy and stability of vehicle following modeling in micro-traffic-simulation, a car following model based on bidirectional GRU(Gated Recurrent Unit) and residual fitting is proposed. This method combines deep learning with traditional kinematics-based car-following models, and uses a bidirectional GRU network to learn the residual between the prediction of physical model and the true value,which is tested on the NGSIM public data set. By comparing the residual fitting performances under IDM, GIPPS and GM car following models, experiments show that the bidirectional GRU network can effectively correct the deviation of the original prediction results. Using the bidirectional GRU network to fit the residuals of the IDM, a Bi-GRU-IDM model is proposed. By comparing with the existing SVR model, BPNN model, and GRU model, the results show that Bi-GRU-IDM model significantly outperforms the other three model both in prediction accuracy and generalization ability.
Key words : bidirectional GRU；residual learning,；car following model,；micro-simulation
