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基于大数据技术的铁路货运价格策略应用研究 被引量:7

A Study on the Big Data Application Platform for Railway Pricing-policy Making
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摘要 精准制定价格策略,对于实现企业效益最大化、保持企业竞争力具有重要意义。随着信息化蓬勃发展,日趋成熟的大数据技术为铁路货运价格策略的制定提供了新的思路和工具。从铁路货运价格决策的业务需求分析出发,借鉴主流大数据Lambda 架构思想,搭建铁路货运价格策略大数据应用平台,针对数据采集、数据分析挖掘等关键技术进行分析,通过设计货运市场运行情况分析、货运价格策略订制、效果预测及监控预警等核心功能,利用大数据为铁路货运价格策略的制定和管理提供辅助支撑。 Targeted pricing policies are of great significance for enterprises to maximize their benefits and remain competitive. With flourishing development of the information technology, more and more advanced big data technologies have begun to provide the policy-makers of railway freight pricing with new ideas and tools. According to a business requirement analysis of railway freight pricing policies, relied on the mainstream big data framework of Lambda, this paper puts forward a big data application platform for making railway freight pricing policies. To shore up policy making and management, this paper also puts forward the core functions of the platform: market performance analysis, freight pricing policy-making, effect prediction and monitoring and early warning based on the analysis of the key technologies including data collection and digging.
作者 伍峰 WU Feng(Freight Department, China State Railway Group Co., Ltd., Beijing 100844, China)
出处 《铁道货运》 2019年第7期6-11,共6页 Railway Freight Transport
基金 中国铁道科学研究院科研项目(2017YJ075)
关键词 铁路货运 价格策略 大数据技术 辅助决策 廉政风险防控 LAMBDA 架构 Railway Freight Transportation Pricing Policy Big Data Technology Decision-making Support Integrity Risk Prevention and Control Lambda Framework
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  • 1丁正中,曾慧.实物期权的三叉树定价模型[J].统计研究,2005,22(11):25-28. 被引量:20
  • 2四兵锋,高自友.市场竞争条件下的客运价格优化策略模型及算法[J].交通运输系统工程与信息,2007,7(1):73-78. 被引量:20
  • 3Mark Hall,Eibe Frank,Geoffrey Holmes,Bernhard Pfahringer,Peter Reutemann,Ian H. Witten.The WEKA data mining software[J]. ACM SIGKDD Explorations Newsletter . 2009 (1)
  • 4Zhang M J,Wang H B,Lu Y,et al.TerraFly GeoCloud:an online spatial data analysis and visualization system. ACM Transactions on Intelligent Systems and Technology . 2015
  • 5Zheng L,Zeng C Q,Li L,et al.Applying data mining techniques to address critical process optimization needs in advanced manufacturing. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’’14) . 2014
  • 6Owen S,Anil R,Dunning T,et al.Mahout in Action. . 2011
  • 7http://www.gartner.com/it-glossary/big-data/ .
  • 8PREKOPCSAK Zoltan,MAKRAI Gabor,HENK Tamas,et al.Radoop:analyzing big data with rapidminer and hadoop. Proceedings of the2nd Rapid Miner Community Meeting and Conference . 2011
  • 9L.Yu,J.Zheng,W.Shen,B.Wu,B.Wang,L.Qian,B.Zhang.BC-PDM:Data mining,social network analysis and text mining system based on cloud computing. the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . 2012
  • 10ZENG C,JIANG Y,ZHENG L,et al.Fiu-miner:A fast,integrated,and user-friendly system for data mining in distributed environment. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . 2013

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