一种基于BP神经网络的铁路大宗货物运价风险预警判定方法

为获取较大的市场占有率和较好的利润增长水平,铁路货运公司需要实时掌握铁路货物运价在货运市场中的竞争力,综合考虑社会、企业自身和货主等因素,基于BP神经网络算法,进行铁路货运价格风险预警判定方法研究,并建立运价风险预警模型。以某铁路局集团有限公司大宗货物运输中的煤炭运输为例,选取2015-2017年相关数据,训练BP神经网络模型,得到铁路煤炭运价的风险预警结果。与实际数据对比,拟合程度较高,因此可使用该方法对当期的运价风险程度进行合理预测,同时也对相关铁路部门的运价政策制定与调整起到辅助决策作用。

Early warning and judgment method for freight rate risk of railway bulk cargo based on BP neural network

In order to obtain a larger market share and a better level of profit growth, railway freight companies need to grasp the competitiveness of railway freight rates in the freight market in real time. Comprehensively considering the factors of society, enterprise itself and cargo owner and other factors, based on BP neural network algorithm, this paper studied the method of railway freight rate risk early warning, and established the freight rate risk early warning model. Taking the coal transportation in the bulk cargo transportation of a railway administration group company as an example, the paper selected the relevant data from 2015 to 2017, trained the BP neural network model, and obtained the risk warning results of the railway coal transportation rate. Compared with the actual data, the early warning results have a high degree of fitting, so this method can be used to make a reasonable prediction of the current freight rate risk level, and also play an auxiliary decision-making role for the formulation and adjustment of freight rate policies of relevant railway departments.