基于大数据的未到达货票清算预测平台研究

基金项目:

中国铁路总公司科技研究开发计划项目(2017Z002-B)

详细信息

Liquidation prediction platform for un-arrived freight invoice based on big data

摘要: 为解决未到达货票承运制清算的预测问题,研究了基于k近邻(k-NN, k-Nearest Neighbor)算法模型的预测算法,应用Hadoop技术,构建了基于大数据的未到达货票清算预测平台。实践表明,该平台可使业务部门及时掌握全路货运营运情况,同时明晰货运承运企业间的经营业绩,是铁路货物运输承运制清算系统的重要组成部分。
- 货运承运制清算 /
- k近邻算法 /
- 大数据
Abstract: In order to solve the problem of liquidation prediction of un-arrived freight invoice carrier system, the forecasting algorithm based on the k-Nearest Neighbor(k-NN) algorithm model was studied. Hadoop technology was applied to construct a liquidation prediction platform for un-arrived freight invoice based on big data. The practice shows that the platform can make the service department master the freight operation situation of the whole railway in time, and clarify the service performance among freight carrier enterprises. It is an important part of the railway freight transportation carrier system liquidation.
- freight transportation carrier system liquidation /
- k-Nearest Neighbor algorithm /
- big data

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