基于LM-BP算法的轨道交通客流短时预测  

Short-term Prediction Research on Urban Rail Transit Passenger Flow Based on LM-BP Algorithm

作　　者：王立政 朱从坤[1] WANG Li-zheng;ZHU Cong-kun(College of Civil Engineering,Suzhou University of Science and Technology,Suzhou 215011, China)

机构地区：[1]苏州科技大学土木工程学院,苏州215011

出　　处：《价值工程》2018年第3期154-156,共3页Value Engineering

摘　　要：BP神经网络是当前比较常用的人工神经网络,针对BP神经网络存在易陷入局部极小值、收敛速度慢等缺陷,将LM算法引入到BP神经网络中,以改进BP神经网络在预测时的训练过程,并利用轨道交通客流的时间序列对其有效性进行验证,结果证明该方法对轨道交通客流的短时预测有着更高的准确度和精度。BP neural network algorithm is the currently common artificial neural network.Referring to the shortcomings existing in BP neural network,such as：local minimum,low convergence rate,this paper introduces LM algorithm to improve BP neural network algorithm,then trains the BP neural network prediction model and applies this method to the time sequence of urban rail transit passenger flow to perform validation.The result shows that the method has a higher accuracy and precision to the short-term prediction of urban rail transit passenger flow.

关 键 词：轨道交通客流 短时预测 BP神经网络 LM算法 

分 类 号：U293.5[交通运输工程—交通运输规划与管理]