Abstract
Predicting traffic data on traffic networks is essential to transportation management. It is a challenging task due to the complicated spatial-temporal dependency. The latest studies mainly focus on capturing temporal and spatial dependencies with spatially dense traffic data. However, when traffic data become spatially sparse, existing methods cannot capture sufficient spatial correlation information and thus fail to learn the temporal periodicity sufficiently. To address these issues, we propose a novel deep learning framework, Multi-component Spatial-Temporal Graph Attention Convolutional Networks (MSTGACN), for traffic prediction, and we successfully apply it to predicting traffic flow and speed with spatially sparse data. MSTGACN mainly consists of three independent components to model three types of periodic information. Each component in MSTGACN combines dilated causal convolution, graph convolution layer, and the weight-shared graph attention layer. Experimental results on three real-world traffic datasets, METR-LA, PeMS-BAY, and PeMSD7-sparse, demonstrate the superior performance of our method in the case of spatially sparse data.
Highlights
Traffic prediction is one of the most essential tasks in the Intelligent Transportation System [1]. e goal of this task is to predict the future traffic conditions by analyzing the historical traffic data
We propose a novel framework called Multicomponent Spatial-Temporal Graph Attention Convolutional Network (MSTGACN), which consists of three relatively independent modules; each module is composed of multiple spatial-temporal graph attention convolution blocks to capture spatial correlations efficiently in the case of spatially sparse data
The contributions of our work can be summarized as follows: (1) We propose a spatial-temporal graph attention convolution block consisting of dilated causal convolution, graph convolution layer, and graph attention layer. e parameters of two graph attention networks (GAT) layers are shared in one block
Summary
Traffic prediction is one of the most essential tasks in the Intelligent Transportation System [1]. e goal of this task is to predict the future traffic conditions (e.g., traffic speed and traffic volume) by analyzing the historical traffic data. Traffic prediction is one of the most essential tasks in the Intelligent Transportation System [1]. Accurate and timely traffic prediction is essential to many realworld applications. Traffic prediction methods [2, 3] can be divided into classic statistical methods and machine learning models, which are limited by the stationarity assumption and fail to capture the spatial correlations. From the perspective of periodicity, some methods use time information of sample data as additional input features [5, 7] to learn periodic information, and some attempts [8, 14, 15] divide the data and model into multiple components to capture the correlations under different periods.
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