【摘要】 最近，基于深度学习的城市流量预测被广泛应用于智慧城市的建设。由于这些方法通常对数据量要求较高，因此难以扩展到数据匮乏的城市。虽然迁移学习可以利用数据丰富的源城市协助目标城市进行城市流量预测，但由于忽略了长距离路网的连通性，因此现有方法的性能无法满足实际使用的需要。为解决这个问题，提出一种基于时空图卷积的迁移预测方法，即在源城市和目标城市之间构建一个共现空间，然后在共现空间中对源城市和目标城市流量数据进行映射对齐，从而实现源城市流量预测模型在目标城市上的迁移预测。具体来说，我们设计了一个动态时空图卷积模块和一个时间编码器，以同时捕捉流量的时间特征和空间特征，这些特征揭示了道路网络结构、人类出行习惯和城市流量之间的内在关联。然后，将这些特征作为跨城市不变表示被非线性映射到共现空间。通过优化马氏距离损失，目标城市特征与源城市特征在共现空间中对齐，从而实现跨城市自行车流量预测。在2015年芝加哥、纽约和华盛顿的公共自行车流量数据集上对所提出的方法进行评估，结果表明该方法明显优于目前最先进的技术。更多还原

【Abstract】 Recently, deep learning based city flow prediction has been extensively used in the establishment of smart cities. These methods are data-hungry, making them unscalable to areas lacking data. Although transfer learning can use data-rich source domains to assist target domain cities in city flow prediction, the performance of existing methods cannot meet the needs of actual use, because the long-distance road network connectivity is ignored. To solve this problem, we propose a transfer learning method based on spatiotemporal graph convolution, in which we construct a co-occurrence space between the source and target domains, and then align the mapping of the source and target domains’ data in this space, to achieve the transfer learning of the source city flow prediction model on the target domain. Specifically, a dynamic spatiotemporal graph convolution module along with a temporal encoder is devised to simultaneously capture the concurrent spatiotemporal features, which implies the inherent relationship among the road network structures, human travel habits, and city bike flow. Then, these concurrent features are leveraged as cross-city invariant representations and nonlinearly spanned to a co-occurrence space. The target domain features are thereby aligned with the source domain features in the co-occurrence space by using a Mahalanobis distance loss, to achieve cross-city bike flow prediction. The proposed method is evaluated on the public bike flow datasets in Chicago, New York, and Washington in 2015, and significantly outperforms state-of-the-art techniques.更多还原