Applying K-Nearest Neighbor Algorithm for Statewide Annual Average Daily Traffic Estimates

Assigning non-ATR sample count sites to different factor groups is an imprecise process. Currently, factor groups are determined on the basis of a combination of geographic location and functional roadway classification. This paper proposes a new K-nearest neighbor algorithm using geographic information system (GIS) technology. Roadway and land use characteristics can be captured in the K-nearest neighbor algorithm for the factor group process. The simulation results show that an unweighted K-nearest neighbor algorithm can produce better AADT estimates than the traditional eighty-four factor approach that uses each functional class as a factor group. The K-nearest neighbor algorithm can be a useful way to carry out roadway classification.

• Corporate Authors:

  Transportation Research Board

  500 Fifth Street, NW
  Washington, DC  United States  20001
• Authors:
  • Jin, Li
  • Fricker, Jon D
• Conference:
  • Transportation Research Board 87th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2008-1-13 to 2008-1-17
• Date: 2008

Language

• English

Media Info

• Media Type: DVD
• Features: Figures; References; Tables;
• Pagination: 19p
• Monograph Title: TRB 87th Annual Meeting Compendium of Papers DVD

Subject/Index Terms

• TRT Terms: Algorithms; Annual average daily traffic; Classification; Data quality; Geographic information systems; Information management; Land use; Statistical sampling; Traffic counting; Types of roads by traffic volume
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; I71: Traffic Theory;

Filing Info

• Accession Number: 01091042
• Record Type: Publication
• Report/Paper Numbers: 08-1915
• Files: BTRIS, TRIS, TRB
• Created Date: Mar 31 2008 8:04AM