Short-term traffic forecasting using self-adjusting k-nearest neighbours

Short-term traffic forecasting is becoming more important in intelligent transportation systems. The k-nearest neighbour (kNN) method is widely used for short-term traffic forecasting. However, the self-adjustment of kNN parameters has been a problem due to dynamic traffic characteristics. This study proposes a fully automatic dynamic procedure kNN (DP-kNN) that makes the kNN parameters self-adjustable and robust without predefined models or training for the parameters. A real-world dataset with more than one year traffic records is used to conduct experiments. The results show that the DP-kNN can perform better than the manually adjusted kNN and other benchmarking methods in terms of accuracy on average. This study also discusses the difference between holiday and workday traffic prediction as well as the usage of neighbour distance measurement.

• Record URL:
  https://doi.org/10.1049/iet-its.2016.0263
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/1751956X
• Supplemental Notes:
  • Abstract reprinted with permission of the Institution of Engineering and Technology.
• Authors:
  • Sun, Bin
  • Cheng, Wei
  • Goswami, Prashant
  • Bai, Guohua
• Publication Date: 2018-2

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: pp 41-48
• Serial:
  • IET Intelligent Transport Systems
  • Volume: 12
  • Issue Number: 1
  • Publisher: Institution of Engineering and Technology (IET)
  • ISSN: 1751-956X
  • EISSN: 1751-9578
  • Serial URL: https://ietresearch.onlinelibrary.wiley.com/journal/17519578
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Artificial intelligence; Intelligent transportation systems; Pattern recognition systems; Traffic forecasting
• Uncontrolled Terms: K-nearest neighbor algorithm
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01666657
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 23 2018 4:44PM