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 T i , traffic flow in c...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 T i , traffic flow in current period Q i , speed in current period V i , density in current period K i , the number of vehicles in current period N i , occupancy in current period R i , traffic state parameter in current period X i , travel time in previous time period T i -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 T i 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.展开更多
作为城市交通规划、建设的依据,居民出行调查显得尤为重要。调查数据的挖掘分析可以为交通结构的改善及交通政策的制定提供一定参考。利用西安市居民出行调查样本数据,借助于Classification And Regression Tree算法,分别构建了可达过...作为城市交通规划、建设的依据,居民出行调查显得尤为重要。调查数据的挖掘分析可以为交通结构的改善及交通政策的制定提供一定参考。利用西安市居民出行调查样本数据,借助于Classification And Regression Tree算法,分别构建了可达过程与乘车过程的决策树模型。该模型结果表明:到站距离为可达过程出行方式选择的主要影响因素;在乘车过程中,出行距离为出行方式的主要影响因素,远距离出行条件下,居民更愿意选择私家车及“公交+地铁”组合出行方式;近距离出行更倾向于选择公交出行。在此基础上,年龄、是否拥有小汽车、有无公交卡等因素对出行方式的选择产生进一步影响。展开更多
基金The National Natural Science Foundation of China(No.51478114,51778136)
文摘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 T i , traffic flow in current period Q i , speed in current period V i , density in current period K i , the number of vehicles in current period N i , occupancy in current period R i , traffic state parameter in current period X i , travel time in previous time period T i -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 T i 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.
文摘作为城市交通规划、建设的依据,居民出行调查显得尤为重要。调查数据的挖掘分析可以为交通结构的改善及交通政策的制定提供一定参考。利用西安市居民出行调查样本数据,借助于Classification And Regression Tree算法,分别构建了可达过程与乘车过程的决策树模型。该模型结果表明:到站距离为可达过程出行方式选择的主要影响因素;在乘车过程中,出行距离为出行方式的主要影响因素,远距离出行条件下,居民更愿意选择私家车及“公交+地铁”组合出行方式;近距离出行更倾向于选择公交出行。在此基础上,年龄、是否拥有小汽车、有无公交卡等因素对出行方式的选择产生进一步影响。
