基于大数据的高速铁路客流分析与辅助决策研究

发布时间：2018-11-20 09:58

【摘要】：近年来,高速铁路以其快捷、准时、安全、环保的特点,在我国乃至世界范围内高速发展。随着我国高速铁路的路网规模逐步扩大和运营里程的增加,铁路旅客运输能力得到逐步释放,铁路旅客运输供不应求的局面得到缓解,铁路运输生产正逐步由粗放型向精细化转换。客流作为铁路运输组织的基础和关键因素,其分析工作是一个复杂的过程,如何对客流的分布特征及变化规律进行系统分析,掌握客流现状与变化趋势,对铁路开行方案、营销策略、客票销售等都具有重要意义。随着信息化发展建设的不断加深,中国铁路客票发售与预定系统TRS累积了大量、完整、一致的历史数据,这可为合理、科学地分析高速铁路客流,获取高质量的辅助决策信息提供数据基础。继物联网、云计算,大数据分析技术成为采集、存储、管理、分析和共享海量数据的核心技术之一。本文拟采用Microsoft SQL Server 2012作为大数据分析工具,构建星型模式铁路客票数据仓库,建立多维数据集,进行客票数据OLAP分析和数据挖掘,并将分析和挖掘结果以规范的、清晰的报表等形式可展示给用户,从而更好的指导铁路的运输调配,为领导的决策提供辅助支持。本文首先简要概括了国内外大数据和铁路客流研究现状以及当前大数据分析技术发展最成熟的数据仓库和数据挖掘技术,并根据高铁客票的实际特点,详细地从旅客出行行为、时空分布特性、客票收入与运能关系、客流预测等方面介绍高速铁路客流分析和挖掘主要研究内容;接着探求如何基于Microsoft SQL Server 2012商务智能工具进行高速铁路客票数据仓库和数据挖掘关键技术开发;最后详细介绍高速铁路客票统计决策分析系统的开发工具和具体功能模块,并采用贵广高铁2016.11.21-2016.11.27—周的客票数据对高速铁路客票统计决策分析系统进行测试。
[Abstract]:In recent years, high-speed railway has developed rapidly in our country and even in the world because of its fast, punctual, safe and environmental protection characteristics. With the expansion of China's high-speed railway network and the increase of mileage, the capacity of railway passenger transport has been gradually released, and the situation that the supply of railway passenger transport is in short supply has been alleviated. Railway transportation production is gradually changing from extensive to refined. As the foundation and key factor of railway transportation organization, passenger flow analysis is a complicated process. How to systematically analyze the distribution characteristics and changing law of passenger flow, master the present situation and change trend of passenger flow, and make a plan for railway operation, Marketing strategy, ticket sales and so on are of great significance. With the development of information technology, China Railway ticket selling and booking system (TRS) has accumulated a large amount of complete and consistent historical data, which can be used to analyze the passenger flow of high-speed railway reasonably and scientifically. Access to high-quality decision-making information provides a data base. Following the Internet of things, cloud computing, big data analysis technology has become one of the core technologies for collecting, storing, managing, analyzing and sharing massive data. In this paper, we use Microsoft SQL Server 2012 as big data analysis tool, construct star model railway ticket data warehouse, establish multidimensional data set, carry out OLAP analysis and data mining of ticket data, and standardize the results of analysis and mining. Clear reports and other forms can be displayed to users, so as to better guide the distribution of railway transportation, and provide support for leaders in decision-making. Firstly, this paper briefly summarizes the current research situation of big data and railway passenger flow at home and abroad, and the most mature data warehouse and data mining technology developed by big data analysis technology at present, and according to the actual characteristics of high-speed railway ticket, it makes a detailed analysis of passenger travel behavior. This paper introduces the main research contents of high-speed railway passenger flow analysis and mining in the aspects of space-time distribution characteristics, the relationship between ticket income and transportation capacity, passenger flow prediction and so on. Then it explores how to develop the key technologies of high-speed railway ticket data warehouse and data mining based on Microsoft SQL Server 2012 business intelligence tool. Finally, the development tools and specific function modules of the high speed railway passenger ticket statistics decision analysis system are introduced in detail. The ticket statistics and analysis system of high speed railway is tested by using the ticket data of Gui-Guang high-speed railway during the period of 11. 11. 21-11. 27-week.
【学位授予单位】：西南交通大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U293.13

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