兰州大学机构库 >数学与统计学院
我国铁路客运量的中短期预测研究
Alternative TitleThe Mid Short Term Prediction of Railway Passenger Traffic Volume in China
席林
Thesis Advisor焦桂梅
2016-05-15
Degree Grantor兰州大学
Place of Conferral兰州
Degree Name硕士
Keyword铁路客运量 混沌粒子群算法 春节因子 校正因子
Abstract铁路客运量的中短期预测是铁路运输公司制定旅客运输计划的基础,更是合理配置人力物力资源及展开旅客运输工作的重要依据。鉴于航空运输事业的快速发展,客运市场竞争愈发激烈,铁路客运部门必须对市场内的客流变化及时采取措施,方能满足市场的需求。 本文分别从年度预测和月度预测两个层面对铁路客运量进行预测,既为铁路的运输长远发展提供了决策支持,更为铁路各部门及时应对瞬息万变的市场提供了可靠依据。 年度数据预测部分,提出了一个新的基于最优模型选取的M-CPSO-GM 模型,并在我国铁路客运量的年度预测中得到了好的效果。 月度数据预测中,首先根据S-ARIMA 模型得到一步预测值,然后对其进行校正。考虑到春节因素,先将数据分成春运月份和非春运月份两部分分开校正。对于春运月份,首次提出了与一步预测误差高度相关的春节因子作为校正因子,通过FNN 对春运月份的一步预测值进行校正;对于非春运月份,则采用上月同比增长率作为校正因子,通过BP 神经网络进行校正。结果表明,校正后的月度数据预测精度有了明显提高。 最后,对论文所做的工作进行了总结,简要说明了文中尚待完善的问题,并指出我国铁路客运量预测以后需要努力的方向。
Other AbstractThe short-term forecast of China's railway passenger traffic is the foundation of the railway company to develop annual plans, as well as the rational allocation of resources, expand the important basis for the work of passenger transport. In view of the development of air transport and passenger transport market competition, the railway passenger transport sector must be an early response to the changes in passenger traffic within the market to react in a timely manner.This paper predicts the railway passenger volume from the two levels of the annual forecast and monthly forecast, respectively. Both providing the decision support for the long-term development of railway transport and providing a reliable basis for the railway departments timely response to the changing market.In the forecast of the annual data, we proposes the M-CPSO-GM model based on the optimal model selection, and has a very good effect in the annual forecast of China's railway passenger traffic volume.In the forecast of the monthly data, we get the first step predictive value according to the S-ARIMA model firstly, then put it into the spring months and other months for processing.For the spring months, this paper puts forward an Spring Festival factor which is highly correlated with the one step prediction error as correction factor to correct the prediction values through fuzzy neural network. For non-spring months, we use the growth rate of last month as the correction factor, corrected by BP neural network. The results show that the prediction accuracy of the corrected monthly data has been significantly improved.Finally, the work of this paper is summarized, and the problems to be improved in this paper are briefly described, and the direction of the future of railway passenger traffic volume prediction in China is pointed out.
URL查看原文
Language中文
Document Type学位论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/224593
Collection数学与统计学院
Recommended Citation
GB/T 7714
席林. 我国铁路客运量的中短期预测研究[D]. 兰州. 兰州大学,2016.
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