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

TL;DR

This paper introduces a novel multivariate graph attention network model that leverages outdoor cellular traffic data to improve spatial-temporal prediction of urban traffic, reducing reliance on costly sensor data.

Contribution

The study proposes a new GAT-based model that captures multivariate correlations and spatial dependencies in outdoor cellular traffic for urban traffic prediction.

Findings

Model significantly outperforms existing methods

Utilizes large-scale cellular traffic data effectively

Enhances spatial-temporal prediction accuracy

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.