基于梯度提升决策树模型的铁路货运装卸时间预测技术  

Prediction technology of railway freight loading and unloading time basedon gradient boosting decision tree model

作　　者：钟立民 付骏峰 李长宇 孔垂云 邵杰 ZHONG Limin;FU Junfeng;LI Changyu;KONG Chuiyun;SHAO Jie(Institute of Computing Technologies,China Academy of Railway Sciences Corporation Limited,Beijing 100081,China;The Center of National Railway Intelligent Transportation System Engineering and Technology,Beijing 100081,China;School of Computer Science and Engineering,University of ElectronicScience and Technology of China,Chengdu 611731,China)

机构地区：[1]中国铁道科学研究院集团有限公司电子计算技术研究所,北京100081 [2]国家铁路智能运输系统工程技术研究中心,北京100081 [3]电子科技大学计算机科学与工程学院,成都611731

出　　处：《铁路计算机应用》2023年第3期1-5,共5页Railway Computer Application

基　　金：国家铁路智能运输系统工程技术研究中心开放基金(2021YJ195)。

摘　　要：铁路货运装卸时间的精准预测可提升铁路货运系统的调度合理性和服务质量,但装卸时间受多种因素影响。文章针对铁路货运装卸时间预测问题,从铁路货运运单全流程信息中挖掘运单属性与货运装卸时间的关系,以分类与回归树为基础模型,在LightGBM框架下构建梯度提升决策树模型;对铁路货运运单全流程信息中的相关数据进行整合、对数变换、增加特征等预处理,形成运单数据集;采用该数据对构建的模型进行训练,结果表明,构建的模型对货运装卸时间的预测性能优于与其对比的其他机器学习模型。将该模型应用在实际货运装卸业务场景时,实际准确率依旧高于其他对比模型。Accurate prediction of railway freight loading and unloading time can improve the scheduling rationality and service quality of railway freight systems,but freight loading and unloading time is affected by various factors.Aiming at the problem of railway freight loading and unloading time prediction,this paper excavated the relationship between freight bill attributes and freight loading and unloading time from the entire process information of freight bills,based on the classification and regression tree model,constructed a gradient boosting decision tree model under the LightGBM framework.The paper integrated,logarithmically transformed,and added features to the relevant data of the entire process of railway freight waybill information to form a waybill dataset,using this dataset to train the constructed model.The results show that the prediction performance of the constructed model for freight loading and unloading time is superior to other machine learning models compared.When this model was applied to actual freight handling business scenarios,the actual accuracy was still higher than other comparison models.

关 键 词：装卸时间 铁路货运 梯度提升决策树模型 集成学习 机器学习 

分 类 号：U294.2[交通运输工程—交通运输规划与管理] TP39[交通运输工程—道路与铁道工程]