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Title: Multi-fold correlation attention network for predicting traffic speeds with heterogeneous frequency
Authors: Sun, Yidan
Jiang, Guiyuan
Lam, Siew-Kei
He, Peilan
Ning, Fangxin
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Sun, Y., Jiang, G., Lam, S., He, P. & Ning, F. (2022). Multi-fold correlation attention network for predicting traffic speeds with heterogeneous frequency. Applied Soft Computing, 124, 108977-.
Journal: Applied Soft Computing
Abstract: Short-term traffic prediction (e.g., less than 15 min) is challenging due to severe fluctuations of traffic data caused by dynamic traffic conditions and uncertainties (e.g., in data acquisition, driver behaviors, etc.). Substantial efforts have been undertaken to incorporate spatiotemporal correlations for improving traffic prediction accuracy. In this paper, we demonstrate that closely located road segments exhibit diverse spatial correlations when characterized using different measurements, and considering these multi-fold correlations can improve prediction performance. We propose new measurements to model multiple spatial correlations among traffic data. We develop a Multi-fold Correlation Attention Network (MCAN) that achieves accurate prediction by capturing multi-fold spatial correlation and multi-fold temporal correlations, and incorporating traffic data of heterogeneous sampling frequencies. The effectiveness of MCAN has been extensively evaluated on two real-world datasets in terms of overall performance, ablation study, sensitivity analysis, and case study, by comparing with several state-of-the-art methods. The results show that MCAN outperforms the best baseline with a reduction in mean absolute error (MAE) by 13% on Singapore dataset and 11% on Beijing dataset.
ISSN: 1568-4946
DOI: 10.1016/j.asoc.2022.108977
Rights: © 2022. Published by Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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