Short-term Hourly Traffic Forecasts using Hong Kong Annual
Traffic Census |
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Authors: | William H K Lam Y F Tang K S Chan Mei-Lam Tam |
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Institution: | (1) Department of Civil and Structural Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, P.R. China |
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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. |
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Keywords: | Annual Traffic Census Auto-Regressive Integrated Moving Average Gaussian Maximum Likelihood Neural Network Non-Parametric Regression |
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