城市轨道交通客流预测算法设计与仿真

Design and Simulation of Passenger Flow Forecast Algorithm for Urban Rail Transit

为了更好地解决城市轨道交通的客流预测问题,提出了基于混合神经网络与卡尔曼滤波器的客流预测多层次模型。首先采用ELAN神经网络实现客流量的初步预测;然后采用卡尔曼滤波器对神经网络预测结果进行修正,以进一步提高预测结果精度;最后为了验证模型的正确性,以上海地铁交通作为研究对象,进行了客流观测和预测模拟。实验结果表明,所提出的多层次模型比单纯其中一种算法能减少约0.8%的误差,并且具有更好的实际效果。

To forecast exactly the passenger flow of the urban rail transit,a hierarchical framework based on neural net- work and Kalman-filter model was presented. First, ELAN neural network model is employed to implement the predic tion of the passenger flow. Then the Kalman-filter was used to refine the forecast data of the passenger flow so as to ad- vance the accuracy of the predicted results. Finally, in order to validate the proposed model, the passenger flow of Shanghai subway transport hub was observed and simulated. Experimental results show that the proposed hierarchical model reduces error about 0. 8% and has better effects in contrast with any single algorithm.