01091042

Component

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.

01084478

08-1915

English

19p

2008

Transportation Research Board 87th Annual Meeting

DVD

Figures (2) ; References (12) ; Tables (9)

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

Data and Information Technology; Highways; Operations and Traffic Management; I71: Traffic Theory

Transportation Research Board Annual Meeting 2008 Paper #08-1915

BTRIS, TRIS, TRB

Jan 29 2008 4:15PM