Predictor fusion for short-term traffic forecasting

(No abstract provided) Highlights: (1) A novel prediction fusion framework is proposed in short-term traffic prediction; (2) The proposed framework cac combine multiple individual predictors; (3) A generic fusion framework for prediction under different traffic conditions; and (4) The kNN fusion method can improve final accuracy, especially during incidents.

• Record URL:
  • https://doi.org/10.1016/j.trc.2018.04.025
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0968090X18305527
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2018 Frangce Guo et al. Published by Elsevier Ltd.
• Authors:
  • Guo, Fangce
  • Polak, John W
  • Krishnan, Rajesh
• Publication Date: 2018-7

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 90-100
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 92
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Accuracy; Data fusion; Mathematical prediction; Traffic forecasting; Traffic incidents
• Uncontrolled Terms: Nearest neighbor approach
• Subject Areas: Highways; Planning and Forecasting;

Filing Info

• Accession Number: 01673849
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 27 2018 4:52PM