Multicomponent Spatial-Temporal Graph Attention Convolution Networks for Traffic Prediction with Spatially Sparse Data

Author:

Liu Shaohua1ORCID,Dai Shijun1ORCID,Sun Jingkai1ORCID,Mao Tianlu2ORCID,Zhao Junsuo3ORCID,Zhang Heng3ORCID

Affiliation:

1. School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

2. Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China

3. Institute of Software Chinese Academy of Sciences, Beijing 100190, China

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.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

General Mathematics,General Medicine,General Neuroscience,General Computer Science

Reference40 articles.

1. Data-Driven Intelligent Transportation Systems: A Survey

2. Discovering spatio-temporal causal interactions in traffic data streams;W. Liu

3. Short-Term Traffic Flow Forecasting: An Experimental Comparison of Time-Series Analysis and Supervised Learning

4. Semi-supervised classification with graph convolutional networks;T. N. Kipf

5. Graph WaveNet for Deep Spatial-Temporal Graph Modeling

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