基于马尔科夫—Verhulst模型的铁路货运量预测研究

铁路货运量是一个地区经济发展的先行指标之一,准确预测铁路货运量能够为该地区的发展规划起到指导作用。针对传统灰色Verhulst模型在进行铁路货运量预测时模型误差较大的问题,运用马尔科夫链模型对传统Verhulst模型的预测结果进行修正改进,以提高模型的预测精度。最后,通过引入实际案例,验证了经过马尔科夫链改进的灰色Verhulst模型在预测精度方面有了大幅度的提高,适用于甘肃省铁路货运量的预测。因此,应用该模型对甘肃省2015年到2017年的铁路货运量进行预测,为该地区的物流运输及其他相关行业的发展提供可靠的指标依据。

Research on Railway Freight Volume Prediction Based on Markov Verhulst Model

Railway freight volume is one of the leading indicators of economic development in an area and accurate prediction of railway freight volume may serve as guidance to the development planning of this area. This paper is focused on the model error of the traditional grey Verhulst model used in railway freight volume forecasting to improve its prediction accuracy. Markov Chain model is used to modify and improve traditional Verhulst model. In the end,practical cases are introduced to verify the significance of Markov Chain to improve the prediction accuracy of the gray verhulst model,which is proved suitable for Gansu railway freight volume forecast. Therefore,the application of this model to predict the railway freight volume in Gansu province from 2015 to 2017 provides reliable indicators for the development of logistics,transportation and other related industries in the region.