基于改进卷积神经网络的短时公交客流预测  

Short-term Bus Passenger Flow Prediction Based on Improved Convolutional Neural Network

作　　者：陈深进 薛洋[1] CHEN Shen-jin;XUE Yang(School of Electronic and Information Engineering,South China University of Technology,Guangzhou 510641,China)

机构地区：[1]华南理工大学电子与信息学院,广州510641

出　　处：《计算机科学》2019年第5期175-184,共10页Computer Science

基　　金：广东省应用型科技研发重大专项资金项目(2015B010131004)资助

摘　　要：针对城市公交客流存在随机性、时变性和不确定性的问题,文中提出了一种基于无监督特征学习理论和改进卷积神经网络的短时公交站点客流预测模型,以为市民提供实时、准确、有效的公交出行服务。运用无监督学习的方法对公交客流出行特征表达进行提取,利用大量已有数据集描述不同日期、不同时间段的短时客流的特征表达。为了防止和减少过拟合现象,运用改进卷积神经网络DropSample训练方法构造一个高效且高可信度的模型预测系统。在训练过程中,使用Adam算法的优化器对模型进行优化,更新网络模型参数,为自适应性学习率设置不同的参数。利用公交客流算法模型对广州实际公交站点的客流进行预测,实验结果表明:改进CNN网络模型的均方根误差为229.539,平均绝对百分比误差为0.117,相比于CNN网络模型、多元线性回归模型、卡尔曼滤波模型和BP神经网络模型,该模型的预测精度和可靠性更高。实例证明所提方法的预测误差更小,改进模型和算法具有实用性和可靠性。Aiming at the random,time-varying and uncertain problems of urban public transport passenger flow,this paper proposed an unsupervised feature learning theory and an improved convolutional neural network based short-term bus station passenger flow prediction,which provides real-time,accurate and effective bus travel services for citizens.In order to prevent and reduce the occurrence of over-fitting,an efficient and reliable model prediction system based on DropSample training method of improved convolutional neural network is constructed.During the training process,the model can be used to describe the short-term passenger flow in different dates and different time periods.The optimizer of Adam algorithm is used to optimize the model,the network model parameters are updated,and different parameters for the adaptive learning rate are set.The results show that the root mean square error of the improved CNN network model is 229.539 and the average absolute percentage error is 0.117.Compared with CNN network model,multiple li-near regression model,Kalman filter model and BP neural network model,this model is more accurate and reliable.The prediction error of the proposed method is smaller,and an example proves that the improved model and algorithm are practical and reliable.

关 键 词：公交客流 卷积神经网络 无监督学习 DropSample训练方法 模型预测系统 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]