Spatio-Temporal AutoEncoder for Traffic Flow Prediction

Forecasting traffic flow is an important task in urban areas, and a large number of methods have been proposed for traffic flow prediction. However, most of the existing methods follow a general technical route to aggregate historical information spatially and temporally. In this paper, we propose a...

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Veröffentlicht in:IEEE transactions on intelligent transportation systems Jg. 24; H. 5; S. 1 - 11
Hauptverfasser: Liu, Mingzhe, Zhu, Tongyu, Ye, Junchen, Meng, Qingxin, Sun, Leilei, Du, Bowen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.05.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Convolution
Correlation
Data mining
Decoding
Feature extraction
hidden state extraction
History
Spatial distribution
spatio-temporal autoencoder
Time series analysis
Traffic flow
Traffic flow prediction
Traffic information
Urban areas
ISSN:1524-9050, 1558-0016
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Zusammenfassung:Forecasting traffic flow is an important task in urban areas, and a large number of methods have been proposed for traffic flow prediction. However, most of the existing methods follow a general technical route to aggregate historical information spatially and temporally. In this paper, we propose a different approach for traffic flow prediction. Our major motivation is to more effectively incorporate various intrinsic patterns in real-world traffic flows, such as fixed spatial distributions, topological correlations, and temporal periodicity. Along this line, we propose a novel autoencoder-based traffic flow prediction method, named Spatio-Temporal AutoEncoder (ST-AE). The core of our method is an autoencoder specially designed to learn the intrinsic patterns from traffic flow data, and encode the current traffic flow information into a low-dimensional representation. The prediction is made by simply projecting the current hidden states to the future hidden states, and then reconstructing the future traffic flows with the trained autoencoder. We have conducted extensive experiments on four real-world data sets. Our method outperforms existing methods in several settings, particularly for long-term traffic flow prediction.
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ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2023.3243913