Application of Neural Networks to Estimate AADT on Low-Volume Roads
- 1 October 2001
- journal article
- Published by American Society of Civil Engineers (ASCE) in Journal of Transportation Engineering
- Vol. 127 (5), 426-432
- https://doi.org/10.1061/(asce)0733-947x(2001)127:5(426)
Abstract
Artificial neural networks are applied as a means of estimating the average annual daily traffic (AADT) volume from short-period traffic counts. Fifty-five automatic traffic recorder sites located on low-volume rural roads in Alberta, Canada are studied. The neural network models used in this study are based on a multilayered, feedforward, and back-propagation design for supervised learning. The AADT estimation errors resulting from various durations and frequencies of counts are analyzed by computing average and percentile errors. The results of this study indicate a clear preference for two 48-h counts as compared to other frequencies (one or three) or durations (24- or 72-h) of counts. In fact, the 95th percentile error values of about 25% for the neural network models compare favorably with the values reported in the literature for low-volume roads using the traditional factor approach. A number of advantages of the neural network approach over the traditional factor approach of AADT estimation are also included in the paper.Keywords
This publication has 5 references indexed in Scilit:
- Neural Networks as Alternative to Traditional Factor Approach of Annual Average Daily Traffic Estimation from Traffic CountsTransportation Research Record: Journal of the Transportation Research Board, 1999
- Accuracy of Estimates of Mean Daily Traffic: A ReviewTransportation Research Record: Journal of the Transportation Research Board, 1997
- Statewide Traffic Volume Studies and Precision of AADT EstimatesJournal of Transportation Engineering, 1996
- A review of neural networks applied to transportTransportation Research Part C: Emerging Technologies, 1995
- Classifying Highways: Hierarchical Grouping versus Kohonen Neural NetworksJournal of Transportation Engineering, 1995