Liu, Y. orcid.org/0000-0002-9367-3532, Chen, H., Wu, S. et al. (5 more authors) (2023) Enhancement of traffic management algorithms for UK motorways. In: Transportation Research Procedia. TRA Lisbon 2022 Conference Proceedings Transport Research Arena (TRA Lisbon 2022), 14-17 Nov 2022, Lisbon, Portugal. Elsevier , pp. 1568-1575.
Abstract
The overall aim of this work is to review and improve traffic CM algorithm to delay the onset of flow breakdown. The results indicated that compared to other two classic models, the Underwood model was able to match the field data on the M25 motorway consistently and capture the speed-flow relationships successfully, in terms of larger R-squared coefficient (R2) and smaller average values of root mean squared error (RMSE). In addition, it was found that Gaussian function can describe the relationship between flow values at the turning points of traffic speed and flow curve and the threshold of the CM algorithm. The fitted traffic speed and flow curve showed that the values at the turning points were significantly reduced under light and heavy rainfall. Thus, the threshold values of the CM algorithm should be optimized according to the determined Gaussian function in the present work.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Authors. Published by Elsevier. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY-NC-ND 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Weather; Traffic flow; Speed; Motorway; Speed-flow pattern |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Spatial Modelling and Dynamics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 04 Mar 2024 13:07 |
Last Modified: | 04 Mar 2024 13:17 |
Published Version: | http://dx.doi.org/10.1016/j.trpro.2023.11.625 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.trpro.2023.11.625 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:209803 |