背景值优化的灰色马尔科夫模型在铁路客流预测中的应用

Application of Grey Markov Model with Background Value Optimization in Railway Passenger Flow Prediction

以提高铁路客流预测精度为出发点,通过对传统的灰色模型进行分析,采用积分的数学思想对灰色预测背景值进行优化.结合马尔科夫预测模型的优点,运用马尔科夫对优化后的灰色预测模型误差进行修正,提高了预测模型的精度.以我国铁路客流预测为实例,通过对预测模型的预测结果的对比研究,验证了模型的有效性.

In order to improve the prediction accuracy of railway passenger flow,the traditional grey model is analyzed,and the grey prediction background value is optimized by integrating the mathematical thinking.Combined with the advantages of Markov chain prediction model,the error of the optimized grey prediction model is corrected by Markov chain to improve the accuracy of the prediction model.Taking the railway passenger flow forecast in China as an example,the validity of the model is verified by the comparison of the prediction results.