基于多变量传播图注意力网络的室外蜂窝流量时空预测
Multivariate and Propagation Graph Attention Network for Spatial-Temporal Prediction with Outdoor Cellular Traffic
摘要：
时空预测是智能交通领域的一个关键问题，有助于交通控制和事故预防等任务的实现。以前的研究依赖于从传感器收集的大规模交通数据。然而，由于设备和维护成本，不太可能在所有地区部署传感器。本文通过从电信公司每天超过20亿条记录中提取的室外蜂窝流量来解决这个问题，因为用户移动性引起的室外蜂窝流量与交通流量高度相关。我们研究了城市道路交叉口，旨在预测未来的室外蜂窝交通的所有交叉口给定的历史室外蜂窝交通。此外，我们提出了一个新的模型，多变量时空预测，主要由两个扩展的图形注意力网络（GAT）。首先，GAT被用来探索多变量蜂窝流量之间的相关性。另一种GAT利用注意力机制进行图传播，以提高捕获空间依赖性的效率。实验表明，该模型显着优于我们的数据集上的最先进的方法。
英文摘要：
Spatial-temporal prediction is a critical problem for intelligent transportation, which is helpful for tasks such as traffic control and accident prevention. Previous studies rely on large-scale traffic data collected from sensors. However, it is unlikely to deploy sensors in all regions due to the device and maintenance costs. This paper addresses the problem via outdoor cellular traffic distilled from over two billion records per day in a telecom company, because outdoor cellular traffic induced by user mobility is highly related to transportation traffic. We study road intersections in urban and aim to predict future outdoor cellular traffic of all intersections given historic outdoor cellular traffic. Furthermore, we propose a new model for multivariate spatial-temporal prediction, mainly consisting of two extending graph attention networks (GAT). First GAT is used to explore correlations among multivariate cellular traffic. Another GAT leverages the attention mechanism into graph propagation to increase the efficiency of capturing spatial dependency. Experiments show that the proposed model significantly outperforms the state-of-the-art methods on our dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
