灰色理论在铁路客运量预测中的分析研究

Analysis of grey theory in railway passenger traffic volume forecast

针对传统灰色预测模型存在对历史数据依赖性强、容错性小及抗干扰能力差的局限性,将无偏灰色理论与残差验证理论引入预测模型,从趋势曲线灰色拟合与状态分类方式上对传统灰色模型进行改进,提出无偏灰色预测铁路客运量方法;同时,对铁路客运量预测方法从定性预测和定量预测两个方面进行阐述并对其优缺点进行分析。依据建立的无偏灰色预测铁路客运量模型,以1997—2016年铁路客运量为基础数据,对铁路"十三五"时期的数据进行预测,通过残差检验和结果分析,其预测精度明显高于BP神经网络预测。

In view of the limitations of traditional grey prediction model, which strongly depended on historical data, had small fault tolerance and poor anti-interference ability, unbiased grey theory and residual verification theory were introduced to the prediction model, and the traditional grey model was improved from the trend curve grey fitting and state classification method, the unbiased grey prediction of railway passenger traffic volume was proposed. The forecast method of railway passenger traffic volume was expounded from two aspects of qualitative prediction and quantitative prediction, and its advantages and disadvantages were analyzed. Based on the unbiased grey forecast model of railway passenger traffic volume and the data of railway passenger traffic volume in 1997-2016, the data of railway during the ＂13th Five-Year＂ period were predicted by the residual test and the result analysis. The prediction accuracy is obviously higher than that of the BP neural network prediction.