Loading and Unloading Time Estimation Based on Xgboost for Railway Freight Transport
The traditional method of loading and unloading time prediction of freight transport,which directly utilizes the standard time specified in the Detailed Instructions Governing Train Operation at Station,cannot properly characterize the time change under the impacts of complex factors,and achieves low prediction accuracy.This paper utilizes the data mining method to gather the relevant data on the loading and unloading time of freight transport from the railway integrated dispatching information system.It also utilizes the boosted decision tree model Xgboost to predict the loading and unloading time of freight transport.Compared with the reference model,the proposed model can achieve substantial improvement in prediction accuracy,and provide more effective support for traffic flow prediction and automatic drawing of train operation charts.
heavy haul railwayfreight transportloading and unloading timedecision treeXgboost
万方数据
维普
