Multivariate and Propagation Graph Attention Network for Spatialoral Prediction with Outdoor Cellular Traffic
Journal
International Conference on Information and Knowledge Management, Proceedings
Pages
3248-3252
Date Issued
2021
Author(s)
Abstract
Spatialoral 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 spatialoral 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. ? 2021 ACM.
Subjects
graph attention network
multivariate spatialoral prediction
outdoor cellular traffic
Backpropagation
Traffic control
Cellulars
Critical problems
Device costs
Graph attention network
Intelligent transportation
Large-scales
Multivariate spatialoral prediction
Outdoor cellular traffic
Propagation graph
Traffic data
Forecasting
Other Subjects
Backpropagation; Traffic control; Cellulars; Critical problems; Device costs; Graph attention network; Intelligent transportation; Large-scales; Multivariate spatialoral prediction; Outdoor cellular traffic; Propagation graph; Traffic data; Forecasting
Type
conference paper