铁路机车车辆需求预测模型的研究
发布时间：2018-11-08 20:04
【摘要】：摘要：铁路是国家的重要基础设施,在综合运输系统中起着主干作用,是适合于我国经济地理特性和人们收入水平的重要运输方法。铁路机车车辆是铁路运输供给实现的物质基础。但目前作为运输载体的铁路机车车辆仍存在着一定程度的购置不合理、闲置、效益低下等,缺乏装备购置的科学的评价体系。 在市场经济条件下,铁道部门一方面满足运能需求,另一方面也要优化社会效益。根据购置的对象不同,机车车辆主要分为机车、客车、货车三类。本文主要预测方法是运用线性回归法,灰色预测法得出结果,再利用组合预测进行加权取值。取得的值为铁路机车车辆的总拥有量,通过对历史保有量,设备更新改造的分析最终得出合理的预测值。 预测以铁路机车为例,分析2020年营业里程、客车周转量、货车周转量取值代入线性回归方程,同时利用灰色预测和组合预测计算2020年铁路机车总拥有量。考虑机车使用寿命,分析铁路机车更新淘汰量,最终计算铁路机车的购置量,得出机车车辆合理的年均购置需求。
[Abstract]:Absrtact: railway is an important national infrastructure, which plays an important role in the integrated transportation system. It is an important transportation method suitable for the economic and geographical characteristics of our country and the income level of people. Railway locomotives and rolling stock are the material basis for the realization of railway transportation supply. But at present, the railway locomotive and rolling stock, as the carrier of transportation, still have some unreasonable purchase, idle, low benefit and so on, and lack of scientific evaluation system of equipment purchase. Under the condition of market economy, the railway department should satisfy the demand of transportation capacity on the one hand, and optimize the social benefit on the other. According to the object of purchase, locomotive and rolling stock are divided into three categories: locomotive, passenger car and freight car. In this paper, the main prediction method is to use linear regression method, grey forecasting method to get the results, and then use the combined forecast to weight the value. The obtained value is the total ownership of railway locomotive and rolling stock. Taking railway locomotives as an example, this paper analyzes the business mileage, the turnover of passenger cars and the turnover of freight cars in 2020. The linear regression equation is used to calculate the total ownership of railway locomotives in 2020 by using grey forecast and combined forecast. Considering the service life of locomotives, the replacement and elimination of railway locomotives are analyzed, and the purchase quantity of railway locomotives is calculated finally, and the reasonable annual purchase demand of locomotive and rolling stock is obtained.
【学位授予单位】：北京交通大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：F224;F532
本文编号：2319520
[Abstract]:Absrtact: railway is an important national infrastructure, which plays an important role in the integrated transportation system. It is an important transportation method suitable for the economic and geographical characteristics of our country and the income level of people. Railway locomotives and rolling stock are the material basis for the realization of railway transportation supply. But at present, the railway locomotive and rolling stock, as the carrier of transportation, still have some unreasonable purchase, idle, low benefit and so on, and lack of scientific evaluation system of equipment purchase. Under the condition of market economy, the railway department should satisfy the demand of transportation capacity on the one hand, and optimize the social benefit on the other. According to the object of purchase, locomotive and rolling stock are divided into three categories: locomotive, passenger car and freight car. In this paper, the main prediction method is to use linear regression method, grey forecasting method to get the results, and then use the combined forecast to weight the value. The obtained value is the total ownership of railway locomotive and rolling stock. Taking railway locomotives as an example, this paper analyzes the business mileage, the turnover of passenger cars and the turnover of freight cars in 2020. The linear regression equation is used to calculate the total ownership of railway locomotives in 2020 by using grey forecast and combined forecast. Considering the service life of locomotives, the replacement and elimination of railway locomotives are analyzed, and the purchase quantity of railway locomotives is calculated finally, and the reasonable annual purchase demand of locomotive and rolling stock is obtained.
【学位授予单位】：北京交通大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：F224;F532
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