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Estimation of AADT from short period counts in hong kong - A comparison between neural network method and regression analysis.
- Source :
- Journal of Advanced Transportation; Jun2000, Vol. 34 Issue 2, p249-268, 20p
- Publication Year :
- 2000
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- TRAFFIC congestion
REGRESSION analysis
ARTIFICIAL neural networks
Subjects
Details
- Language :
- English
- ISSN :
- 01976729
- Volume :
- 34
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Journal of Advanced Transportation
- Publication Type :
- Academic Journal
- Accession number :
- 63461456
- Full Text :
- https://doi.org/10.1002/atr.5670340205