Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/153498
Title: Predicting traffic congestion evolution : a deep meta learning approach
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). Predicting traffic congestion evolution : a deep meta learning approach. The Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21), 3031-3037. https://dx.doi.org/10.24963/ijcai.2021/417
Abstract: Many efforts are devoted to predicting congestion evolution using propagation patterns that are mined from historical traffic data. However, the prediction quality is limited to the intrinsic properties that are present in the mined patterns. In addition, these mined patterns frequently fail to sufficiently capture many realistic characteristics of true congestion evolution. In this paper, we propose a representation learning framework to characterize and predict congestion evolution between any pair of road segments. Specifically, we build dynamic attributed networks (DAN) to incorporate both dynamic and static impact factors while preserving dynamic topological structures. We propose a Deep Meta Learning Model (DMLM) for learning representations of road segments which support accurate prediction of congestion evolution. DMLM relies on matrix factorization techniques and meta-LSTM modules to exploit temporal correlations at multiple scales, and employ meta-Attention modules to merge heterogeneous features while learning the time-varying impacts of both dynamic and static features. Compared to all state-of-the art methods, our framework achieves significantly better prediction performance on two congestion evolution behaviors (propagation and decay) when evaluated using real-world dataset.
URI: https://hdl.handle.net/10356/153498
DOI: 10.24963/ijcai.2021/417
Rights: © 2021 International Joint Conferences on Artificial Intelligence. All rights reserved. This paper was published in Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21) and is made available with permission of International Joint Conferences on Artificial Intelligence.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

Files in This Item:
File Description SizeFormat 
0417_IJCAI2021.pdf1.12 MBAdobe PDFView/Open

Page view(s)

47
Updated on May 15, 2022

Download(s) 50

53
Updated on May 15, 2022

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.