臺灣高鐵的營運受限法令的規範，目前票價的訂定採用標準距離費率制度(per mile rates system)，儘管臺灣高鐵實施許多不同的定價策略，但是票價只能在法令規範之範圍內進行訂定。良好的定價應該根據顧客眼中的價值，提供不同等級版本的商品以區隔不同的市場。然而目前鐵路業較少有研究探討如何進行各起訖站點(Origin- Destination)路線的定價，本研究將參考零售業單一產品定價及航空業的單一航線的定價模型，建構本研究的鐵路的定價模型。此模型以商務旅客及休閒旅客服的服務價值作為定價的主要依據，考量兩個售票時間內的兩類旅客購票行為，利用賽局理論建構三期的不合作賽局，採用反向歸納法的技巧進行求解。然而反向歸納法無法直接找出最佳的定價，因此將根據反向歸納法的原理，採用對基因演算法及單行法的混和方法進行求解。數據顯示不論在何種情境下，皆具有唯一的最佳定價方法，此結果是由於休閒旅客及商務旅客出現的時間點不同，可以利用價格區隔兩個售票時間購票的旅客類型，進而完全擷取休閒旅客其商務旅客購票的消費者剩餘，使業者達到營收最大。

Taiwan High Speed Rail (THSR) is restricted on its operation by the regulations, the current ticket fare is set by the per mile rates system. Although THSR has implemented many different pricing strategies, fares can only be priced within upper 20% due to the regulations. A good pricing should consider customer’s valuation to provide different level versions of products for market segmentation. However, there are few studies on pricing origin-destination routes in railway. This study will establish a pricing model in railway through reviewing the pricing models in retail and aviation. The main basis of pricing in this model is business travelers’ and leisure travelers’ service valuation. This study can construct a three-stage non-cooperative game to take into account the buying behaviour of these two types of travelers in two selling period and use backward induction to solve, however, it cannot directly find the optimal pricing. Therefore, a hybrid method is used by genetic algorithm and simplex method based on backward induction to solve the game. The numerical results show that there is the same unique optimal pricing strategy, regardless of any circumstances; due to leisure travelers and business travelers appear at different time in two selling period. As a result, THSR can use lower fare in the early selling period to attract leisure travelers and higher fare in the later selling period to attract business travelers and price out leisure travelers. Therefore, THSR can extract full consumer surplus of two types of travelers and maximize revenue.

1. 台灣高鐵網站。
2. 交通部高速鐵路工程局網站。
3. 林奕伶(譯)(2013)，訂價背後的心理學(L. Caldwell)，台北市：大是文化(原著出版年：2012)。
4. 張有恆(2013)，現代運輸學(三版)，台北市：華泰文化
5. 陳建華、高自友(2005)，合理制定鐵路旅客票價的優化方法，鐵道學報，27(4), 16-19。
6. 郭家旻(2012)，探討不同退費策略下對鐵路業者營收影響之研究，國立成功大學交通管理科學研究所碩士論文。
7. 蘇瑩瑩(2014)，隨機團體需求之定位限制，國立成功大學交通管理科學研究所碩士論文。
8. Bazaraa, M. S., Jarvis, J. J., & Sherali, H. D. (2011). Linear programming and network flows. John Wiley & Sons.
9. Beasley, D., Martin, R. R., & Bull, D. R. (1993). An overview of genetic algorithms： Part 1. Fundamentals. University computing, 15, 58-58.
10. Bharill, R., & Rangaraj, N. (2008). Revenue management in railway operations： A study of the Rajdhani Express, Indian Railways. Transportation Research Part A： Policy and Practice, 42(9), 1195-1207.
11. Bingfeng, S. I., & Ziyou, G. A. O. (2007). Optimal model for passenger transport pricing under the condition of market competition. Journal of Transportation Systems Engineering and Information Technology, 7(1), 72-78.
12. Bitran, G., & Caldentey, R. (2003). An overview of pricing models for revenue management. Manufacturing & Service Operations Management, 5(3), 203-229.
13. Bland, R. G. (1977). New finite pivoting rules for the simplex method. Mathematics of operations Research, 2(2), 103-107.
14. Botimer, T. C., & Belobaba, P. P. (1999). Airline pricing and fare product differentiation： A new theoretical framework. Journal of the Operational Research Society, 50(11), 1085-1097.
15. Briginshaw, D. (2013). Ouigo leads the way in low-fare high-speed rail, International Railway Journal, Retrieved (2014/10/02) form
http：//www.railjournal.com/index.php/high-speed/ouigo-leads-the-way-in-low-fare-high-speed-rail.html
16. Chen, Y., & Xie, J. (2008). Online consumer review： Word-of-mouth as a new element of marketing communication mix. Management science, 54(3), 477-491.
17. Chiambaretto, P., & Fernandez, A. S. (2014). Transferring low-cost marketing practices from air to rail services： The Ouigo case. Research in Transportation Business & Management, 10, 40-44.
18. Choi, S., & Mattila, A. S. (2004). Hotel revenue management and its impact on customers’ perceptions of fairness. Journal of Revenue and pricing Management, 2(4), 303-314.
19. Côté, J. P., Marcotte, P., & Savard, G. (2003). A bilevel modelling approach to pricing and fare optimisation in the airline industry. Journal of Revenue and Pricing Management, 2(1), 23-36.
20. Dantzig, G. B. (1998). Linear programming and extensions. Princeton university press.
21. Desiraju, R., & Shugan, S. M. (1999). Strategic service pricing and yield management. The Journal of Marketing, 44-56.
22. Fay, S., & Xie, J. (2010). The economics of buyer uncertainty： Advance selling vs. probabilistic selling. Marketing Science, 29(6), 1040-1057.
23. Geetha, T., Mahalakshmi, K., Ummusalma, I., & Kumaran, K. M. (2013). An Observational Analysis of Genetic Operators. International Journal of Computer Applications, 63(18).
24. Graham, R. J., Garrow, L. A., & Leonard, J. D. (2010). Business travelers’ ticketing, refund, and exchange behavior. Journal of Air Transport Management, 16(4), 196-201.
25. Guo, L. (2009). Service cancellation and competitive refund policy. Marketing Science, 28(5), 901-917.
26. Haley, R. I. (1968). Benefit segmentation： A decision-oriented research tool. The Journal of Marketing, 30-35.
27. Hillier, F. S., & Lieberman, G. J. Introdction to operations research. McGraw-Hill.
28. Iliescu, D. C., Garrow, L. A., & Parker, R. A. (2008). A hazard model of US airline passengers’ refund and exchange behavior. Transportation Research Part B： Methodological, 2(3), 229-242
29. Kimes, S. E. (1989). Yield management： a tool for capacity-considered service firms. Journal of operations management, 8(4), 348-363.
30. Lazarev, J. (2013). The welfare effects of intertemporal price discrimination： an empirical analysis of airline pricing in US monopoly markets. New York University.
31. Morrison, S. A., & Winston, C. (1990). The dynamics of airline pricing and competition. The American Economic Review, 80(2), 389-393.
32. Nuzzolo, A., Crisalli, U., & Gangemi, F. (2000). A behavioural choice model for the evaluation of railway supply and pricing policies. Transportation Research Part A： Policy and Practice, 34(5), 395-404.
33. Pels, E., & Rietveld, P. (2004). Airline pricing behaviour in the London–Paris market. Journal of Air Transport Management, 10(4), 277-281.
34. Shugan, S. M., & Xie, J. (2000). Advance pricing of services and other implications of separating purchase and consumption. Journal of Service Research,2(3), 227-239.
35. Shugan, S. M., & Xie, J. (2004). Advance selling for services. California Management Review, 46(3), 37-54.
36. Shugan, S. M., & Xie, J. (2005). Advance-selling as a competitive marketing tool. International Journal of Research in Marketing, 22(3), 351-373.
37. Stavins, J. (2001). Price discrimination in the airline market： The effect of market concentration. Review of Economics and Statistics, 83(1), 200-202.
38. Stigler, G. J. (1952). The theory of price. Retrieved from (2014/08/07)
http：//14.139.56.90/handle/1/2024167
39. Su, M., Zheng, X., & Sun, L. (2014). Coupon trading and its impacts on consumer purchase and firm profits. Journal of Retailing, 90(1), 40-61.
40. Suzuki, Y., Crum, M. R., & Audino, M. J. (2004). Airport leakage and airline pricing strategy in single-airport regions. Transportation Research Part E： Logistics and Transportation Review, 40(1), 19-37.
41. Talluri, K., & Van Ryzin, G. (2004). Revenue management under a general discrete choice model of consumer behavior. Management Science, 50(1), 15-33.
42. Talluri, K. T., & Van Ryzin, G. J. (2006). The theory and practice of revenue management. Springer Science & Business Media.
43. Van Vliet, V. (2011). Service Marketing mix-7 P’s. Retrieved (2015/05/01) from ToolsHero
http：//www.toolshero.com/marketing/service-marketing-mix-7ps/
44. Van Vuuren, D. (2002). Optimal pricing in railway passenger transport： theory and practice in The Netherlands. Transport Policy, 9(2), 95-106.
45. Xie, J., & Gerstner, E. (2007). Service escape： Profiting from customer cancellations. Marketing Science, 26(1), 18-30.
46. Xie, J., & Shugan, S. M. (2001). Electronic tickets, smart cards, and online prepayments： When and how to advance sell. Marketing Science, 20(3), 219-243.
47. Xueyu, Z., & Jiaqi, Y. (2013). Research on the Bi-level programming model for ticket fare pricing of urban rail transit based on particle swarm optimization algorithm. Procedia-Social and Behavioral Sciences, 96, 633-642.
48. Yen, J., Liao, J. C., Lee, B., & Randolph, D. (1998). A hybrid approach to modeling metabolic systems using a genetic algorithm and simplex method. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 28(2), 173-191.