Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15651
Title: Short-term hourly traffic forecasts using Hong Kong Annual Traffic Census
Authors: Lam, WHK 
Tang, YF
Chan, KS
Tam, ML
Keywords: Annual traffic census
Auto-regressive integrated moving average
Gaussian maximum likelihood
Neural network
Non-parametric regression
Issue Date: 2006
Publisher: Springer
Source: Transportation, 2006, v. 33, no. 3, p. 291-310 How to cite?
Journal: Transportation 
Abstract: The need for acquiring the current-year traffic data is a problem for transport planners since such data may not be available for on-going transport studies. A method is proposed in this paper to predict hourly traffic flows up to and into the near future, using historical data collected from the Hong Kong Annual Traffic Census (ATC). Two parametric and two non-parametric models have been employed and evaluated in this study. The results show that the non-parametric models (Non-Parametric Regression (NPR) and Gaussian Maximum Likelihood (GML)) were more promising for predicting hourly traffic flows at the selected ATC station. Further analysis encompassing 87 ATC stations revealed that the NPR is likely to react to unexpected changes more effectively than the GML method, while the GML model performs better under steady traffic flows. Taking into consideration the dynamic nature of the common traffic patterns in Hong Kong and the advantages/disadvantages of the various models, the NPR model is recommended for predicting the hourly traffic flows in that region.
URI: http://hdl.handle.net/10397/15651
ISSN: 0049-4488
DOI: 10.1007/s11116-005-0327-8
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