摘要
针对承运制清算对铁路局集团公司货物运输(简称:货运)收入及营业收入的影响,为贯彻落实“以承运盈余目标为导向”的货运经营理念,实现货运经营效益最大化,文章运用多种机器学习回归算法,针对每项货运承运费用分别构建预测模型,并根据不同算法模型在测试集上的多项评估指标的表现,选定一种最佳模型,进行货运承运运费和承运对外付费每日按票预测,从而提前掌握每日承运盈余结果。研究表明,极端森林回归(ETR,Extra Trees Regressor)算法多项评估指标均表现最好,运用其构建的模型可更为精准地实现货运承运日盈余预测。截至2024年4月,该预测模型共完成约1033万张货票相关承运费用的预测,整体预测误差率在1.7%以下,充分发挥了数据要素价值,为货运效益分析及经营决策等提供数据支撑。
In order to implement the freight management concept of"surplus oriented transportation"and maximize the efficiency of freight management,this paper used multiple machine learning regression algorithms to construct prediction models for each freight transportation cost,and selected the best model based on the performance of different algorithm models on multiple evaluation indicators on the test set,predicted freight charges and external payments by ticket on a daily basis,in order to gain an early understanding of the daily shipping surplus results.The research results indicate that the Extra Trees Regression(ETR)algorithm performs the best in multiple evaluation indicators,and the model constructed using it can implement more accurate prediction of daily freight earnings.As of April 2024,the prediction model has completed predictions for approximately 10.33 million freight bills related to transportation costs,with an overall prediction error rate of less than 1.7%.It fully utilizes the value of data elements and provides data support for freight benefit analysis and business decision-making.
作者
张天军
魏文钰
张雯欣
张中华
谭奇龙
ZHANG Tianjun;WEI Wenyu;ZHANG Wenxin;ZHANG Zhonghua;TAN Qilong(Institute of Information Technology,China Railway Urumqi Group Co.Ltd.,Urumqi 830000,China)
出处
《铁路计算机应用》
2024年第11期98-102,共5页
Railway Computer Application
基金
乌鲁木齐局集团公司科技研究开发计划课题(2023-kj-59)。
关键词
承运日盈余
机器学习
特征工程
回归预测
大数据
daily surplus of transportation
machine learning
feature engineering
regression prediction
big data
