Thesis (Ph. D.)--University of Hong Kong, 2008. / Also available in print.

Thesis (Ph. D.)--University of Hong Kong, 2008. / Also available online.

Thesis (Ph. D.)--University of Hong Kong, 2005. / Title proper from title frame. Also available in printed format.

Thesis (M.S.) in Resource Economics and Policy--University of Maine, 2006. / Includes vita. Includes bibliographical references (leaves 117-123).

published_or_final_version / abstract / Civil Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / Urban Planning and Environmental Management / Doctoral / Doctor of Philosophy

Thesis (Ph. D.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

Thesis (M. P. A.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

Thesis (M.S.)--West Virginia University, 2004. / Title from document title page. Document formatted into pages; contains viii, 111 p. Includes abstract. Includes bibliographical references (p. 109-111).

Efficient and comprehensive forecasting of information is of great importance to traffic management. Three types of forecasting methods based on irregularly spaced data—for situations when traffic detectors cannot be installed to generate regularly spaced data on all roads—are studied in this thesis, namely, the single segment forecasting method, multi-segment forecasting method and model-based forecasting method. The proposed models were tested using Global Positioning System (GPS) data from 400 Hong Kong taxis collected within a 2-kilometer section on Princess Margaret Road and Hong Chong Road, approaching the Cross Harbour Tunnel. The speed limit for the road is 70 km/h. It has flyovers and ramps, with a small number of merges and diverges. There is no signalized intersection along this road section. A total of 14 weeks of data were collected, in which the first 12 weeks of data were used to calibrate the models and the last two weeks of data were used for validation. The single-segment forecasting method for irregularly spaced data uses a neural network to aggregate the predicted speeds from the naive method, simple exponential smoothing method and Holt’s method, with explicit consideration of acceleration information. The proposed method shows a great improvement in accuracy compared with using the individual forecasting method separately. The acceleration information, which is viewed as an indicator of the phase-transition effect, is considered to be the main contribution to the improvement. The multi-segment forecasting method aggregates not only the information from the current forecasting segment, but also from adjacent segments. It adopts the same sub-methods as the single-segment forecasting method. The forecasting results from adjacent segments help to describe the phase-transition effect, so that the forecasting results from the multi-segment forecasting method are more accurate than those that are obtained from the single segment forecasting method. For one-second forecasting length, the correlation coefficient between the forecasts from the multi-segment forecasting method and observations is 0.9435, which implies a good consistency between the forecasts and observations. While the first two methods are based on pure data fitting techniques, the third method is based on traffic models and is called the model-based forecasting method. Although the accuracy of the one-second forecasting length of the model-based method lies between those of the single-segment and multi-segment forecasting methods, its accuracy outperforms the other two for longer forecasting steps, which offers a higher potential for practical applications. / published_or_final_version / Civil Engineering / Master / Master of Philosophy