基于Hadoop平台的铁路车流运行径路获取与预测模型及算法研究
发布时间：2018-08-22 12:04
【摘要】：近年来铁路总公司积极推进货物运输改革,努力提高铁路货运服务水平,加强铁路物流化建设,力求扩大铁路运输在货物运输市场中的份额。但是目前铁路货运中最突出的问题是货物运输的时间具有很大的不确定性,大大制约了铁路运输的竞争力。虽然铁路部门制定了铁路货物运到期限,但是货物运输时间仍会受到车流集结、车流改编等运输作业的影响,因此铁路部门需要加强对车流的有效掌控,提升运输组织效率。而准确的车流预测可以实现对车流分布的有效掌控,可以及时避免车流拥堵的发生,保证路网的运输效率。车流预测的基础是车流径路,确定合理的车流径路,对于准确地进行车流预测、高效地进行运输组织具有重要作用。本文以确定合理的车流径路为研究目标,借助铁路运输信息集成平台提供的各业务系统整合的数据,构建车流运行径路获取模型来获取车流运行过程中的真实走行径路,并利用Hadoop平台处理车流运行径路大数据集,对车流经由的每个车站构建针对不同货种的车流运行径路模式和概率后缀树,并在车流预测系统中进行验证。具体工作如下:(1)对反映车流实际走行情况的车流运行径路进行研究,借助铁路运输信息集成平台获取经过整合和共享的铁路业务数据,建立径路节点映射模型、车辆报文匹配模型、径路序列拼接模型来获取完整的车流运行径路。(2)考虑到铁路车流运行径路的数据量会随着时间的累积不断增大,传统数据分析方法不能实现对不断增加的海量数据的有效分析,本文设计大数据分析方法利用Hadoop平台来处理车流运行径路数据,利用Sqoop工具实现传统关系型数据库与分布式文件系统HDFS之间的数据传输,利用MapReduce编程模型高效处理车流运行径路大数据集,利用变阶马尔科夫模型对车流运行径路的径路序列进行处理,建立运行径路模式并构建概率后缀树,用以预测车流运行径路。(3)利用Java编程实现基于运输信息集成平台的车流运行径路获取过程,搭建Hadoop平台进行MapReduce编程开发来处理车流径路大数据,并实现车流运行径路模式提取和概率后缀树的构建,以不同货种、不同径路模式预测车流运行径路,并在车流预测系统中进行验证。
[Abstract]:In recent years, the railway corporation has actively promoted the reform of freight transport, made great efforts to improve the level of railway freight service, strengthened the construction of railway logistics, and made every effort to expand the share of railway transportation in the freight transport market. However, the most prominent problem in railway freight transportation is the uncertainty of the time of freight transportation, which greatly restricts the competitiveness of railway transportation. Although the railway department has established the railway freight delivery deadline, but the freight transport time will still be affected by the train flow assembly, the train flow adaptation and other transportation operations, so the railway department needs to strengthen the effective control of the train flow and improve the transport organization efficiency. The accurate forecast of traffic flow can effectively control the distribution of traffic flow, avoid traffic congestion in time, and ensure the transportation efficiency of the road network. The basis of the traffic flow prediction is the traffic flow path and the determination of the reasonable traffic flow path plays an important role in accurate traffic flow prediction and efficient transportation organization. Based on the integrated data of various business systems provided by the railway transportation information integration platform, this paper constructs a model to obtain the real path of the train flow in the course of the train flow operation, aiming at the determination of the reasonable path of the vehicle flow, and with the help of the integrated data of the various business systems provided by the railway transportation information integration platform, The Hadoop platform is used to deal with the big data set of the train flow running path, and the train flow path mode and probability suffix tree for different kinds of goods are constructed for each station through which the traffic flow is processed, and verified in the traffic flow prediction system. The main works are as follows: (1) the paper studies the running path of the train flow which reflects the actual traffic flow, obtains the integrated and shared railway business data with the aid of the railway transportation information integration platform, and establishes the mapping model of the path node. Vehicle message matching model, path sequence splicing model to obtain the complete train flow running path. (2) considering that the amount of data of railway train flow running path will increase with time, The traditional data analysis method can not realize the effective analysis of the increasing mass data. In this paper, the big data analysis method is designed to use the Hadoop platform to deal with the traffic flow path data. The data transmission between traditional relational database and distributed file system (HDFS) is realized by using Sqoop tool, and the MapReduce programming model is used to efficiently deal with the big data set of train flow running path. The variable order Markov model is used to deal with the train flow path sequence, the running path pattern is established and the probability suffix tree is constructed. It is used to predict the running path of the vehicle flow. (3) the process of obtaining the running path of the train flow based on the integrated platform of transportation information is realized by using Java programming, and the Hadoop platform is built for MapReduce programming to deal with the big data of the train flow path. The train flow path pattern extraction and the construction of probability suffix tree are realized. The traffic flow path pattern is predicted by different goods and different path modes, and verified in the traffic flow prediction system.
【学位授予单位】：北京交通大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U294.1;TP311.13
[Abstract]:In recent years, the railway corporation has actively promoted the reform of freight transport, made great efforts to improve the level of railway freight service, strengthened the construction of railway logistics, and made every effort to expand the share of railway transportation in the freight transport market. However, the most prominent problem in railway freight transportation is the uncertainty of the time of freight transportation, which greatly restricts the competitiveness of railway transportation. Although the railway department has established the railway freight delivery deadline, but the freight transport time will still be affected by the train flow assembly, the train flow adaptation and other transportation operations, so the railway department needs to strengthen the effective control of the train flow and improve the transport organization efficiency. The accurate forecast of traffic flow can effectively control the distribution of traffic flow, avoid traffic congestion in time, and ensure the transportation efficiency of the road network. The basis of the traffic flow prediction is the traffic flow path and the determination of the reasonable traffic flow path plays an important role in accurate traffic flow prediction and efficient transportation organization. Based on the integrated data of various business systems provided by the railway transportation information integration platform, this paper constructs a model to obtain the real path of the train flow in the course of the train flow operation, aiming at the determination of the reasonable path of the vehicle flow, and with the help of the integrated data of the various business systems provided by the railway transportation information integration platform, The Hadoop platform is used to deal with the big data set of the train flow running path, and the train flow path mode and probability suffix tree for different kinds of goods are constructed for each station through which the traffic flow is processed, and verified in the traffic flow prediction system. The main works are as follows: (1) the paper studies the running path of the train flow which reflects the actual traffic flow, obtains the integrated and shared railway business data with the aid of the railway transportation information integration platform, and establishes the mapping model of the path node. Vehicle message matching model, path sequence splicing model to obtain the complete train flow running path. (2) considering that the amount of data of railway train flow running path will increase with time, The traditional data analysis method can not realize the effective analysis of the increasing mass data. In this paper, the big data analysis method is designed to use the Hadoop platform to deal with the traffic flow path data. The data transmission between traditional relational database and distributed file system (HDFS) is realized by using Sqoop tool, and the MapReduce programming model is used to efficiently deal with the big data set of train flow running path. The variable order Markov model is used to deal with the train flow path sequence, the running path pattern is established and the probability suffix tree is constructed. It is used to predict the running path of the vehicle flow. (3) the process of obtaining the running path of the train flow based on the integrated platform of transportation information is realized by using Java programming, and the Hadoop platform is built for MapReduce programming to deal with the big data of the train flow path. The train flow path pattern extraction and the construction of probability suffix tree are realized. The traffic flow path pattern is predicted by different goods and different path modes, and verified in the traffic flow prediction system.
【学位授予单位】：北京交通大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U294.1;TP311.13
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