Long-Term Ship Speed Prediction for Intelligent Traffic Signaling

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 the authors' 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.

• Record URL:
  https://doi.org/10.1109/TITS.2016.2560131
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2016, IEEE.
• Authors:
  • Gan, Shaojun
  • Liang, Shan
  • Li, Kang
  • Deng, Jing
  • Cheng, Tingli
• Publication Date: 2017-1

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: pp 82-91
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 18
  • Issue Number: 1
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Intelligent control systems; Mathematical prediction; Ship pilotage; Ships; Signals; Speed; Waterways
• Uncontrolled Terms: Multilayer perceptron model; Speed prediction models
• Geographic Terms: Yangtze River
• Subject Areas: Marine Transportation; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01625985
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 15 2017 5:03PM