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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. [ABSTRACT FROM AUTHOR]

Most transport models for short-term strategic planning are required to be calibrated with current planning parameters and updated traffic data. There are basically two steps involved in transport model calibration, namely, trip matrix updating and network calibration. These two steps are carried out iteratively in the conventional approach and may not achieve a stable solution. This paper proposes a bilevel programming model that integrates the two steps of trip matrix updating and network calibration into one stage. A solution algorithm is developed on the basis of genetic algorithms. Numerical examples are presented to illustrate the performance of the proposed model and solution algorithm under various conditions where different source data are available. [ABSTRACT FROM AUTHOR]