2019-ZJU_SummerResearch:任务是按照论文中的说明重述论文并重做实验

上传者: 42099087 | 上传时间: 2023-06-21 21:59:31 | 文件大小: 3.39MB | 文件类型: ZIP
2019ZJU_夏季研究 任务是按照论文中的说明重述论文并重做实验。 描述 在教授的指导下,使用Tensorflow实施时空多图卷积网络,以预测乘车需求。 使用的软件包和方法:Tensorflow(1.14),LSTM(单向,三层),GCN(Chevb-net) 纸: 文献评论 本文提出了一种新颖的深度学习模型,称为时空多图卷积网络(ST-MGCN),用于更好地进行区域级乘车需求预测。 而且,该网络更多地关注于遥远区域之间的非欧几里德成对相关性,而不是像许多先前的研究那样关注欧几里德相关性。 这项研究中的挑战来自复杂的时空相关性。 一方面,在不同区域之间观察到复杂的依赖性。 另一方面,不同的时间观测值之间也存在非线性相关性。 为了解决这些挑战,团队首先将非欧几里得相关性(如邻域,功能相似性和区域之间的运输连通性)编码为多个图形。 然后,对于每张图,研究人员使用上下文门控的递归神经网络,

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