Offor, K. orcid.org/0000-0001-9112-070X, Vaci, L. orcid.org/0000-0003-3375-379X and Mihaylova, L. orcid.org/0000-0001-5856-2223 (2019) Traffic estimation for large urban road network with high missing data ratio. Sensors, 19 (12). 2813. ISSN 1424-8220
Abstract
Intelligent transportation systems require the knowledge of current and forecasted traffic states for effective control of road networks. The actual traffic state has to be estimated as the existing sensors does not capture the needed state. Sensor measurements often contain missing or incomplete data as a result of communication issues, faulty sensors or cost leading to incomplete monitoring of the entire road network. This missing data poses challenges to traffic estimation approaches. In this work, a robust spatio-temporal traffic imputation approach capable of withstanding high missing data rate is presented. A particle based approach with Kriging interpolation is proposed. The performance of the particle based Kriging interpolation for different missing data ratios was investigated for a large road network comprising 1000 segments. Results indicate that the effect of missing data in a large road network can be mitigated by the Kriging interpolation within the particle filter framework.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 The Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | particle filtering; road traffic; state estimation; Bayesian inference; Kriging; missing data imputation |
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) The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number European Commission - Horizon 2020 688082 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 08 Jul 2019 15:16 |
Last Modified: | 08 Jul 2019 15:16 |
Status: | Published |
Publisher: | MDPI AG |
Refereed: | Yes |
Identification Number: | 10.3390/s19122813 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:147767 |