Gan, S, Liang, S, Li, K et al. (2 more authors) (2017) Long-Term Ship Speed Prediction for Intelligent Traffic Signaling. IEEE Transactions on Intelligent Transportation Systems, 18 (1). pp. 82-91. ISSN 1524-9050
Abstract
Yangtze River is probably the world's busiest inland waterway. Ships need to be guided when passing through a controlled waterway based on their long-term speed prediction. Inaccurate ship speed prediction leads to nonoptimal traffic signaling, which may cause a significant traffic jam. For the existing intelligent traffic signaling system, the ship speed is assumed to be constant, which has caused many problems and issues. This paper proposes a novel algorithm to construct an improved multilayer perceptron (MLP) network for accurate long-term ship speed prediction, in which the hidden neurons of the MLP are optimized by the particle swarm optimization method. The effectiveness and efficiency of the method are guaranteed by using the orthogonal least squares method, which is the fast approach for the construction of the MLP network in a stepwise forward procedure. The model is driven by easily acquired dynamic data of the ships, including the speed and the position. The effectiveness of the proposed method is further confirmed by comparing with several traditional modeling techniques. To the best of our knowledge, this is the first time that a ship speed model is built for long-term prediction. The experimental results show that the developed model is in good agreement with the real-life data, with more than 97% accuracy. It will help to generate the optimal traffic commands for Yangtze River in an intelligent traffic signaling system.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Keywords: | Multilayer perceptron (MLP); orthogonal least square (OLS); particle swarm optimization (PSO); artificial neural networks (ANNs); ship speed prediction |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 26 Nov 2018 10:54 |
Last Modified: | 26 Nov 2018 10:54 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Identification Number: | 10.1109/TITS.2016.2560131 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:139090 |