Zhu, Y., Wang, P. and Mihaylova, L. orcid.org/0000-0001-5856-2223 (2021) A convolutional neural network combined with a Gaussian process for speed prediction in traffic networks. In: 2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). IEEE International Conference on Multisensor Fusion and Integration (MFI 2021), 23-25 Sep 2021, Karlsruhe, Germany (online). Institute of Electrical and Electronics Engineers (IEEE) ISBN 9781665445221
Abstract
This paper proposes a traffic speed prediction framework combining a Convolutional Neural Network (CNN) with a Gaussian Process (GP) and is an extension of ConvNetGP [1]. The main focus is on spatio-temporal large scale traffic networks and on uncertainty quantification. The emphasis is on the impact on the measurement noises on the predicted traffic speeds. The Gaussian Process regression provides a variance which characterises the accuracy of the prediction. The traffic speed data is converted into a three dimensional format like images and these are inputs of the CNN-GP framework for traffic networks. The CNN-GP framework provides 18.23% average improvement of the speed root mean square error compared with the generic CNN and gives a quantitative characterisation of the noise effects.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Traffic Prediction; Gaussian Process; Deep Neural Network; Large Scale; Scalability |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Funding Information: | Funder Grant number Engineering and Physical Sciences Research Council EP/T013265/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 15 Sep 2021 07:23 |
Last Modified: | 15 Nov 2022 01:13 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/MFI52462.2021.9591204 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:178098 |