计算机科学 ›› 2021, Vol. 48 ›› Issue (4): 70-77.doi: 10.11896/jsjkx.200200024
李冰荣, 皮德常, 候梦如
LI Bing-rong, PI De-chang, HOU Meng-ru
摘要: 移动对象目的地预测是基于位置的服务的重要组成部分。该领域一直存在数据稀疏、长期依赖等难以解决的问题。为了有效解决这些问题,首先引入了一种基于最小描述长度策略(Minimum Description Length,MDL)的轨迹分段方法,以获得轨迹的最佳分段,提高轨迹之间的相似度,实现对轨迹的简化。随后将分段后的数据进行图像化处理和局部特征提取,并对轨迹目的地进行聚类,从而为轨迹数据增加标签。最后提出了一种基于卷积和长短期记忆循环单元的深度学习算法CNN-LSTM,该算法先将局部图像数据和标签作为卷积神经网络(Convolutional Neural Network,CNN)模型的输入,通过空间特征的深度提取来保留有效信息,再利用长短期记忆网络(Long-Short Term Memory,LSTM)算法进行训练和目的地预测。在移动对象的真实轨迹数据集上进行了大量实验,结果表明,所提CNN-LSTM方法具有较强的学习能力,能更好地捕捉轨迹时空相关性。与现有的最新相关算法相比,该方法具有很高的目的地预测准确度。
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