ARIMA-RBF模型在城市轨道交通客流预测中的应用

客流量预测是城市轨道交通规划设计和运营管理的基本依据，已成为城市轨道交通建设过程中的重要环节。本文通过分析平常日客流变化的周规律、非平稳性等时序特征以及ARIMA模型和RBF模型的作用机理，将适合进行线性时间序列预测的ARIMA模型和适合处理非线性问题的RBF神经网络组合，建立了ARIMA RBF预测模型，并用该模型对北京市城市轨道交通平常日客流量进行预测，该模型充分考虑到城市轨道交通客流变化的线性及非线性特征，取得了较好的预测效果。

Application of an ARIMA-RBF model in the forecast of urban rail traffic volume

Passenger flow forecast is the basic reference for the design and operational management of urban rail transit and has become an important procedure in the construction of urban rail transit. We combine a linearity based ARIMA model and a non linearity based RBF neural network and establish an ARIMA RBF prediction model by the analysis of such time sequence characteristics as the weekly change and non stability of passenger flow and the mechanism of ARIMA and RBF models. We then apply the prediction model to the forecast for passenger flow of Beijing daily urban rail transit and receive a better prediction result, fully considering the linear and non linear characteristics of urban rail transit passenger flow.