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Title: Learning traffic network embeddings for predicting congestion propagation
Authors: Sun, Yidan
Jiang, Guiyuan
Lam, Siew-Kei
He, Peilan
Keywords: Engineering::Computer science and engineering::Computer applications::Social and behavioral sciences
Issue Date: 2021
Source: Sun, Y., Jiang, G., Lam, S. & He, P. (2021). Learning traffic network embeddings for predicting congestion propagation. IEEE Transactions On Intelligent Transportation Systems.
Journal: IEEE Transactions on Intelligent Transportation Systems 
Abstract: Traffic congestion has become a global concern due to continuous increase in traffic demand and limited road capacity. The ability to predict traffic congestion propagation, which depicts the spatiotemporal evolution of the congestion scenario, is essential for developing smart traffic management systems and enabling road users to make informed route choices. In this work, we study the behavior of congestion propagation at the road segment level, and leverage this to develop a novel machine learning framework that characterizes and predicts the congestion evolution among different road segments in the traffic network. In particular, our framework can infer the likelihood of congestion propagation between any pair of road segments through single or multiple propagation paths. The proposed framework relies on a network embedding module to learn a representation for each road segment, and a propagation model which calculates the congestion propagation likelihood based on the learned representations. Specifically, an asymmetric embedding of local proximity and global tendency (AE-LPGT)is relied upon for learning low dimension embeddings of the road segments which incorporate various realistic properties of congestion propagations, such as the local proximity property, global propagation tendency, and asymmetric transitivity of congestion propagations. Experimental results with Singapore traffic data show that our method significantly outperforms the state-of-the-art, and the congestion propagation properties in our embeddings have significant impact on the prediction performance.
ISSN: 1524-9050
DOI: 10.1109/TITS.2021.3105445
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:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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