Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/153857
Title: Learning congestion propagation behaviors for traffic prediction
Authors: Sun, Yidan
He, Peilan
Jiang, Guiyuan
Lam, Siew-Kei
Keywords: Engineering::Computer science and engineering::Computer applications::Social and behavioral sciences
Issue Date: 2021
Source: Sun, Y., He, P., Jiang, G. & Lam, S. (2021). Learning congestion propagation behaviors for traffic prediction. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC). https://dx.doi.org/10.1109/ITSC48978.2021.9565132
Abstract: Traffic prediction is a challenging task as the traffic flow is influenced by many seasonal, stochastic, and structural factors. In addition, the spatial and temporal distribution of traffic flow can induce direct and indirect congestion propagation patterns. While existing works have attempted to model spatial-temporal graphs to capture the spatial correlations and temporal dependencies, they fail to consider congestion propagation behavior among road segments. In this paper, we propose a novel traffic prediction model that takes into account the congestion propagation tendencies to improve prediction accuracy. A novel diffusion graph convolution network model is developed to capture the spatial traffic correlations while considering the congestion propagation behavior. Our model also jointly learns the importance of seasonal traffic speed correlations, road contextual information (structural information), and stochastic factors (external factors) through an attention layer. Experimental results on real-world data-set demonstrate the superiority of our method over state-of-the-art traffic prediction techniques, and confirm the significance of congestion propagation behavior in traffic prediction.
URI: https://hdl.handle.net/10356/153857
ISBN: 978-1-7281-9142-3
DOI: 10.1109/ITSC48978.2021.9565132
Rights: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/ITSC48978.2021.9565132.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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