东南大学学报（英文版）2019，Vol.35Issue(3)：393-398,6.DOI:10.3969/j.issn.1003-7985.2019.03.017
基于梯度提升决策树的高速公路行程时间预测模型
Travel time prediction model of freeway based on gradient boosting decision tree
摘要
Abstract
To investigate the travel time prediction method of the freeway,a model based on the gradient boosting decision tree (GBDT) is proposed.Eleven variables (namely,travel time in current period Ti,traffic flow in current period Qi,speed in current period Vi,density in current period Ki,the number of vehicles in current period Ni,occupancy in current period Ri,traffic state parameter in current period Xi,travel time in previous time period Ti-1,etc.) are selected to predict the travel time for 10 min ahead in the proposed model.Data obtained from VISSIM simulation is used to train and test the model.The results demonstrate that the prediction error of the GBDT model is smaller than those of the back propagation (BP) neural network model and the support vector machine (SVM) model.Travel time in current period Ti is the most important variable among all variables in the GBDT model.The GBDT model can produce more accurate prediction results and mine the hidden nonlinear relationships deeply between variables and the predicted travel time.关键词
梯度提升决策树/行程时间预测/高速公路/交通状态参数Key words
gradient boosting decision tree (GBDT)/travel time prediction/freeway/traffic state parameter分类
交通工程引用本文复制引用
程娟,陈先华..基于梯度提升决策树的高速公路行程时间预测模型[J].东南大学学报（英文版）,2019,35(3):393-398,6.基金项目
The National Natural Science Foundation of China(No.51478114,51778136). （No.51478114,51778136）
