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).

Thesis (Ph. D.)--University of Texas at Austin, 1999. / Vita. Includes bibliographical references (leaves 189-197). Available also in a digital version from Dissertation Abstracts.

Thesis (M.A.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 74-76).

Thesis (M. Sc.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 146-149).

Thesis (M.A. in National Security Affairs)--Naval Postgraduate School, December 2003. / Thesis advisor(s): Mikhail Tsypkin, Robert E. Looney. Includes bibliographical references (p. 75-79). Also available online.

Thesis (M. Soc. Sc.)--University of Hong Kong, 1984. / Typewritten.

Thesis (M.U.D.)--University of Hong Kong, 1994. / Includes bibliographical references (leaves 158-159).

Thesis (Ph. D.)--University of Texas at Austin, 2002. / Vita. Includes bibliographical references. Available also from UMI Company.

The creation of a new travel website in the early 21st century can naively be mistaken as an easy open window to the world. LuminariaTravel.com is an online travel website recently created to inform visitors about San Antonio, Texas tourism. This paper seeks to analyze the challenges and offer solutions in entering an online market already entered. This paper presents insight into understanding the tourism industry online, and current marketing methods being used. In order for the small, privately-owned travel website to gain online visitors, LuminariaTravel.com must consider utilizing marketing tools for an integrated marketing plan. Luminariatravel.com must also be correctly positioned in the online travel market to have a unique stance and be able to communicate information that will set it apart from other San Antonio travel websites. This paper will include literature reviews and an industry case study. A final recommended marketing plan and strategy will be offered to LuminariaTravel.com / text

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