Wang, P., Kim, Y., Vaci, L. et al. (2 more authors) (2018) Short-term traffic prediction with vicinity Gaussian process in the presence of missing data. In: 2018 Sensor Data Fusion: Trends, Solutions, Applications (SDF). 12th Symposium Sensor Data Fusion: Trends, Solutions, and Applications, 09-11 Oct 2018, Bonn, Germany. IEEE ISBN 978-1-5386-9398-8
Abstract
This paper considers the problem of short-term traffic flow prediction in the context of missing data and other measurement errors. These can be caused by many factors due to the complexity of the large scale city road network, such as sensors not being operational and communication failures. The proposed method called vicinity Gaussian Processes provides a flexible framework for dealing with missing data and prediction in vehicular traffic network. First, a weighted directed graph of the network is built up. Next, a dissimilarity matrix is derived that accounts for the selection of training subsets. A suitable cost function to find the best subsets is also defined. Experimental results show that with appropriately selected subsets, the prediction root mean square error of the traffic flow obtained by the vicinity Gaussian Processes method reaches 18.9% average improvement with lower costs, which is with comparison to inappropriately chosen training subsets.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 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. |
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 EUROPEAN COMMISSION - HORIZON 2020 688082 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 25 Sep 2018 10:21 |
Last Modified: | 30 Jan 2019 16:29 |
Published Version: | https://doi.org/10.1109/SDF.2018.8547118 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/SDF.2018.8547118 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:136057 |