【摘要】 在对站台短时客流特性进行分析的基础上,基于卡尔曼滤波理论,提出了改进卡尔曼滤波短时客流预测模型,并给出了模型的求解过程.选取北京市客流量较大、客流变化明显的岛式站台、侧式站台、普通站台、换乘站台进行数据采集和实例分析.结果表明,该预测模型的平均绝对误差为0.299,均方误差为34.094,均等系数为0.923,提出的模型可以有效地对短时地铁客流进行预测.相较于传统卡尔曼滤波预测方法,改进的卡尔曼滤波短时客流预测方法能够提升预测信息的实时性,并使平均绝对误差降低了0.448,进一步提高了预测精度.更多还原

【Abstract】 Based on kalman filtering theory,a improved Kalman filter short-term prediction model is put forward and the solving process is presented after the characteristic analysis of rail transit platform.The data acquisition and example analysis are carried out on the island platform,side platform,common platform and transfer platform with large passenger flow and obvious change of passenger flow in Beijing.The results show that the average absolute error of the model is 0.299,the mean square error is 34.094,and the equal coefficient is 0.923,which reveals that the proposed model can effectively predict the short-term subway passenger flow.Compared with the traditional Kalman filtering prediction method,the improved Kalman filter short-term passenger flow forecasting method can improve the real-time information of prediction,reduce the average absolute error by 0.448,and has higher prediction accuracy.更多还原