Multivariate and Propagation Graph Attention Network for Spatial-Temporal Prediction with Outdoor Cellular Traffic

• URL: http://arxiv.org/abs/2108.08307v2
• Date: Fri, 20 Aug 2021 02:44:31 GMT
• Title: Multivariate and Propagation Graph Attention Network for Spatial-Temporal Prediction with Outdoor Cellular Traffic
• Authors: Chung-Yi Lin, Hung-Ting Su, Shen-Lung Tung, Winston H. Hsu
• Abstract summary: This paper addresses the problem via outdoor cellular traffic distilled from over two billion records per day in a telecom company. We study road intersections in urban and aim to predict future outdoor cellular traffic of all intersections given historic outdoor cellular traffic. Experiments show that the proposed model significantly outperforms the state-of-the-art methods on our dataset.
• Score: 25.081221761654756
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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