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Link speed prediction for signalized urban traffic network using a hybrid deep learning approach
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China.
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering. Smart Transportation Research Institute, Enjoyor Co. Ltd, Hangzhou, 310030, China and Engineering Research Center of Intelligent Transport of Zhejiang Province, Hangzhou 310030, China..ORCID iD: 0000-0002-1375-9054
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China.
College of Information Engineering, Zhejiang University of Technology, Hangzhou 310013, China ; Enjoyor Co., Ltd, Hangzhou 310030, China..
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2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Predicting traffic speed is of importance in transportation management. Signalized road networks manifest highly dynamic speed patterns that are challenging to model and predict. We propose a hybrid deep-learning-based approach for link speed prediction, aiming at capturing heterogeneous spatiotemporal correlations between road intersections. After transforming original road networks and intersections into graphs, this approach leverages a layered graph convolution network structure to model traffic speed variations at both intersection and road network levels. The two levels are combined through a fully connected neural layer. Neural spatiotemporal attention mechanisms are applied to modulate the most relevant periodical traffic information during signal cycles. The proposed approach was evaluated using real-world speed data collected in Hangzhou City, China. Experiments demonstrate that the proposed approach can offer a scalable and effective solution for predicting short-term speed for signalized road networks.

Place, publisher, year, edition, pages
2019.
National Category
Transport Systems and Logistics Computer Sciences
Research subject
Transport Science, Transport Systems
Identifiers
URN: urn:nbn:se:kth:diva-256047DOI: 10.1109/ITSC.2019.8917509ISI: 000521238102042Scopus ID: 2-s2.0-85076802347OAI: oai:DiVA.org:kth-256047DiVA, id: diva2:1343496
Conference
22nd IEEE Intelligent Transportation Systems Conference (ITSC 2019), Auckland, New Zealand 27-30 October, 2019
Note

QC 20191003. QC 20200429

Available from: 2019-08-16 Created: 2019-08-16 Last updated: 2020-04-29Bibliographically approved

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fulltext(829 kB)60 downloads
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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf