AST-GCN: Attribute-Augmented Spatiotemporal Graph Convolutional Network for Traffic Forecasting

Traffic forecasting is a fundamental and challenging task in the field of intelligent transportation. Accurate forecasting not only depends on the historical traffic flow information but also needs to consider the influence of a variety of external factors, such as weather conditions and surrounding...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.35973-35983
Main Authors: Zhu, Jiawei, Wang, Qiongjie, Tao, Chao, Deng, Hanhan, Zhao, Ling, Li, Haifeng
Format: Article
Language:English
Subjects:
Artificial neural networks
Convolution
Data models
external factors
Forecasting
graph convolutional network
Mathematical model
Predictive models
Recurrent neural networks
Roads
Source code
spatiotemporal models
Spatiotemporal phenomena
Task analysis
Traffic flow
Traffic forecasting
Traffic information
Traffic speed
Weather
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Traffic forecasting is a fundamental and challenging task in the field of intelligent transportation. Accurate forecasting not only depends on the historical traffic flow information but also needs to consider the influence of a variety of external factors, such as weather conditions and surrounding POI distribution. Recently, spatiotemporal models integrating graph convolutional networks and recurrent neural networks have become traffic forecasting research hotspots and have made significant progress. However, few works integrate external factors. Therefore, based on the assumption that introducing external factors can enhance the spatiotemporal accuracy in predicting traffic and improving interpretability, we propose an attribute-augmented spatiotemporal graph convolutional network (AST-GCN). We model the external factors as dynamic attributes and static attributes and design an attribute-augmented unit to encode and integrate those factors into the spatiotemporal graph convolution model. Experiments on real datasets show the effectiveness of considering external information on traffic speed forecasting tasks when compared with traditional traffic prediction methods. Moreover, under different attribute-augmented schemes and prediction horizon settings, the forecasting accuracy of the AST-GCN is higher than that of the baselines. The source code of the AST-GCN is available at https://github.com/lehaifeng/T-GCN/AST-GCN .
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3062114