Estimation of AADT from short period counts in Hong Kong - a comparison between neural network method and regression analysis

Research output: Journal article publicationJournal articleAcademic researchpeer-review

41 Citations (Scopus)


The average annual daily traffic (AADT) volumes can be estimated by using a short period count of less than twenty-four hour duration. In this paper, the neural network method is adopted for the estimation of AADT from short period counts and for the determination of the most appropriate length of counts. A case study is carried out by analysing data at thirteen locations on trunk roads and primary roads in urban area of Hong Kong. The estimation accuracy is also compared with the one obtained by regression analysis approach. The results show that the neural network approach consistently performed better than the regression analysis approach.
Original languageEnglish
Pages (from-to)249-268
Number of pages20
JournalJournal of Advanced Transportation
Issue number2
Publication statusPublished - 1 Jan 2000

ASJC Scopus subject areas

  • Automotive Engineering
  • Economics and Econometrics
  • Mechanical Engineering
  • Computer Science Applications
  • Strategy and Management

Cite this