铁路货运站场车辆终到停留时间预测模型研究
Prediction model of dwell time for final arrival vehicle in railway freight yard
吴志伟1
作者信息
- 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
- 折叠
摘要
为应对显著增加的铁路货运作业需求,提高铁路货运站场作业效率,在收集相关影响因素的基础上,设计了铁路货运站场车辆终到停留时间预测模型(简称:预测模型).该模型通过统计数据,预测车辆出发空重状态,再根据出发状态预测终到停留时长.采用同时训练随机森林和BP(Back Propagation)神经网络、选取较优结果的方式构建 3个子模型.通过数据验证,该预测模型的预测结果均方误差与平均绝对误差均优于仅使用随机森林算法或BP神经网络算法的模型,能够有效预测车辆终到停留时间,为货运站场作业计划安排和作业效率分析提供技术支撑.
Abstract
To cope with the significantly increased demand for railway freight operations and improve the efficiency of railway freight yard operations,this paper designed a prediction model of dwell time for final arrival vehicles in railway freight yard based on the collection of relevant influencing factors.This model predicted the empty and heavy status of the vehicle's departure based on statistical data,and then predicted the duration of stay.The paper constructed three sub models by simultaneously training a random forest and a BP(Back Propagation)neural network,and selecting the optimal results.Through data verification,the mean square error and mean absolute error of the prediction model are superior to models that only use random forest algorithm or BP neural network algorithm.It can effectively predict the dwell time for final arrival vehicle and provide technical support for the planning and efficiency analysis of freight station operations.
关键词
铁道运输/车辆终到停留时间/随机森林/BP神经网络/货运站场Key words
railway transportation/dwell time for final arrival vehicle/random forest model/Back Propagation(BP)neural network model/freight yard引用本文复制引用
基金项目
中国国家铁路集团有限公司科技研究计划重点课题(N2021X031)
中国国家铁路集团有限公司科技研究计划重大课题(K2022X017)
出版年
2024
NETL
NSTL
万方数据