计算机科学 ›› 2019, Vol. 46 ›› Issue (5): 175-184.doi: 10.11896/j.issn.1002-137X.2019.05.027
陈深进, 薛洋
CHEN Shen-jin, XUE Yang
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。
中图分类号:
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