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AIS data driven general vessel destination prediction: A random forest based approach

  • Published:2020-09 Issue: Volume:118 Page:102729
  • ISSN:0968-090X
  • Container-title:Transportation Research Part C: Emerging Technologies
  • language:en
  • Short-container-title:Transportation Research Part C: Emerging Technologies

Author:

Zhang Chengkai,Bin Junchi,Wang Wells,Peng Xiang,Wang Rui,Halldearn Richard,Liu Zheng

Funder

NSERC

Publisher

Elsevier BV

Subject

Computer Science Applications,Transportation,Automotive Engineering,Civil and Structural Engineering

Reference65 articles.

1. AIS Hub-AIS Data Exchange, 2019. Ais coverage map. http://www.aishub.net/coverage. Accessed: 2019-02-19.

2. Estimated time of arrival using historical vessel tracking data;Alessandrini;IEEE Trans. Intell. Transp. Syst.,2018

3. Random multiclass classification: generalizing random forests to random mnl and random nb;Anita;Database Expert Syst. Appl.,2007

4. Review and perspective for distance-based clustering of vehicle trajectories;Besse;IEEE Trans. Intell. Transp. Syst.,2016

5. Analysis of a random forests model;Biau;J. Mach. Learn. Res.,2012

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