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Forecast and Analysis of Coal Traffic in Daqin Railway Based on the SARIMA-Markov Model

机译:基于Sarima-Markov模型的大秦铁路煤炭交通预测与分析

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摘要

With the continuous advancement of China’s supply-side structural reform, the country’s energy consumption structure has undergone considerable changes, including an overall reduction in fossil energy use and a rapid increase in clean energy application. In the context of China’s coal overcapacity, port and rail capacities are difficult to change in the short term. This study forecasts the monthly coal traffic of Daqin Railway on the basis of the seasonal autoregressive integrated moving-average Markov model and then uses the monthly coal transport data of this railway from September 2009 to November 2019 as samples for model training and verification. Coal traffic from December 2019 to September 2020 is accurately predicted. This study also analyzes the effects of China’s industrial structure adjustment, clean energy utilization, and low-carbon usage on the coal transport volume of Daqin Railway. In addition, the characteristics of seasonal fluctuation and the development trend of Daqin Railway’s coal traffic are explored. This study provides a reference for adjusting the train operation chart of Daqin Railway’s coal transport and developing a special coal train operation plan. It can determine the time of coal transport peak warning, improve the efficiency of coal transport management, and eventually realize a reasonable allocation of resources for Daqin Railway.
机译:随着中国供应方结构改革的不断发展,该国的能源消耗结构发生了相当大的变化,包括化石能源使用的总体减少和清洁能源应用的快速增加。在中国煤产能过剩的背景下,短期内港口和铁路能力难以改变。本研究预测了大秦铁路的每月煤炭交通,基于季节性自回归综合移动平均马尔可夫模型,然后利用2009年9月至2019年11月作为模型培训和验证的样本的每月煤炭运输数据。 2019年12月至9月2020年12月的煤炭交通准确预测。本研究还分析了中国产业结构调整,清洁能源利用和低碳对大秦铁路煤炭运输量的影响。此外,探讨了大秦铁路煤炭交通的季节波动的特点。本研究为调整大秦铁路煤炭运输的列车运行表以及开发特殊煤列车运营计划的参考。它可以确定煤炭运输峰值警告的时间,提高煤炭运输管理的效率,最终实现了大秦铁路的合理配置资源。

著录项

  • 作者

    Cheng Zhang; Shouchen Liu;

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  • 年度 2020
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  • 原文格式 PDF
  • 正文语种 eng
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