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Online learning has become a ubiquitous part of the educational landscape and how teachers are supported in developing approaches to teaching online is a fundamental aspect of the students' learning experience. Based on the implementation of a professional development course on becoming an online teacher offered in a blended learning mode at one university in Hong Kong, this article proposes that offering this type of professional development in a blended mode is very effective in facilitating enhanced usage of the university's learning management system. In a blended mode, teachers are actively engaged with blended learning and were found to make more extensive use of features/ tools in Blackboard after they attended the professional development course. Results support that offering professional development in a blended mode provides teachers with an authentic student perspective, at the same time as they take guided steps in the teacher's role in blended learning.

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. Copyright Springer 2006Annual Traffic Census, Auto-Regressive Integrated Moving Average, Gaussian Maximum Likelihood, Neural Network, Non-Parametric Regression,

This paper investigates the use of real-time automatic vehicle identification (AVI) data and an offline travel time database for real-time estimation of arterial travel times in Hong Kong, China. The offline database consists of average link travel times and spatial link travel time covariance matrices by time of day, day of week, and week of month. Three-month historical travel time estimates and real-time AVI data are adopted for calibration and updating of the spatial covariance relationships of link travel times on Hong Kong arterial roads. A case study has been carried out on a selected path in a Hong Kong urban area to evaluate the performance of three alternative methods for real-time estimation of arterial travel times: fixed offline database (Method 1), continuously updated offline database (Method 2), and continuously updated offline database generated by the nonparametric regression method (Method 3). The validation results show that the travel time estimation errors of Methods 2 and 3 are significantly reduced when compared with those using the fixed offline database.

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