Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31834
Dynamic Analyses for Passenger Volume of Domestic Airline and High Speed Rail

Authors: Shih-Ching Lo


Discrete choice model is the most used methodology for studying traveler-s mode choice and demand. However, to calibrate the discrete choice model needs to have plenty of questionnaire survey. In this study, an aggregative model is proposed. The historical data of passenger volumes for high speed rail and domestic civil aviation are employed to calibrate and validate the model. In this study, different models are compared so as to propose the best one. From the results, systematic equations forecast better than single equation do. Models with the external variable, which is oil price, are better than models based on closed system assumption.

Keywords: forecasting, passenger volume, dynamic competition model, external variable, oil price

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1323


[1] Buckeye, K.R. (1992), Ranking Alternatives Using Fuzzy Numbers, Fuzzy Set and Systems, No. 15, pp. 21-31.
[2] Kanafani, A. and Youssef, W., 1994," High-Speed Rail in California", University of California Institute of Transportation Studies Review, Vol.no17, pp.2-8.
[3] Lin, J. J., Feng, C. M., and Huang L. C., "Forecasting the Taiwan High Speed Rail System on Local Development," Transportation Planning Journal (Taiwan), Vol. 34, No. 3, pp. 391-412 (2005).
[4] Ministry of Transportation and Communications, A Report on the Survey of High Speed Rail Travel Behavior, Taipei, Taiwan (2007).
[5] Lan, L.W. (1991). Impact of High Speed Railway on the Western Corridor of Taiwan Area, Road News Quarterly, Vol. 30, No.1, pp. 15-22.
[6] Wang, C.T. and Liu T.C. (1999). The Aviation Market Characteristics of Taiwan Area and Development Research, Transportation Planning Journal, Vol.28, No.3, pp. 451-484.
[7] Huang, T.C. (2006). Website of Land/Air Transportation Transition Analysis in Response to the Commencement of Revenue Operation of High Speed Railway, Transportation Comments, Chinese Institute of Transportation.
[8] Karlafftis, M.G. and Sinha, K.C. (1997). Modeling Approach Transit Rolling-Stock Deterioration Prediction, Journal of Transportation Engineering, 123, 223-228.
[9] Lu, C.L. (1997). Study on Patronage Transfer Inclination Model of New Travel Modes, Proceedings of the National Science Council (Part C: Humanities and Social Sciences), Vol.8, No.2, pp. 242-259.
[10] Tuan, L.H. and Wang, Y.J. (1999). Integrate the Travel Mode Selection Models of Revealed Preference and Stated Preference Data, Transportation Planning Journal, Vol.28, No.1, pp. 25-60.
[11] Wen, J.H., Lan, L.W. and Hsu, F.S. (2001). Research of Different Traffic Information Resources with Impact on the Route Selection Behaviors of Intercity Commuters, Proceedings of the 6th ROC Transportation Network Seminar, pp. 1-9´╝îChinese Institute of Transportation.
[12] Hung-Yen Chou, Chiang Fu (2007). A Study of Domestic Air Passenger- Preference for High-Speed Rail Mode in Taiwan, The Journal of Global Business Management, Vol.3, Num.2, pp.147-155.
[13] Oliver Feng-Yeu Shyr* and Meng-Fu Hung, INTERMODAL COMPETITION WITH HIGH SPEED RAIL- A GAME THEORY APPROACH, Journal of Marine Science and Technology, Vol. 18, No. 1, pp. 32-40 (2010)
[14] A. J. Lotka, and A. James, Elements of Mathematical Biology, Dover, publications New York (1956).
[15] A. J. Lotka, Elements of Physical Biology, Williams and Wilkins, Baltimore (1925).
[16] V. Volterra, Mem. R. Acadd. Lincei Series IV 2, 31, (1926).
[17] Bass, F. M. 1969. A new product growth for model consumer durables. Management Sci. 15(5) 215- 227.)
[18] F. M. Bass, Comments on " A New Product Growth for Model Consumer Durables" Manag. Sci., 50, 1833-1840 (2004)
[19] C. Fisher and R. H. Pry, Technological Forecasting and Social Changes, 75-88 (1971).
[20] A. Norton and F. M. Bass, Manage. Sci., 33, 1069-1086 (1987).
[21] V. Mahajan, E. Muller and F. M. Bass, J. Marketing, 54, 1-26 (1990).
[22] N. Meade and T. Islam, Int. J. Forecasting, 22, 519-545 (2006).
[23] P. Parker, and H. Gatignon, Int. J. Res. Marketing, 11, 17-39 (1994).
[24] C. W. I. Piotorius and J. M. Utterback, Res. Policy, 26, 67-84 (1997).
[25] J. R. Williams, Strategic Manage. J., 4, 55-65 (1983).