在台灣城際運輸中存在著顯著的方向性以及尖離峰特性，台灣高鐵列車在尖峰時段頻頻出現列車擁擠的現象，不僅嚴重影響列車服務水準，同時也對車站運轉之安全造成極大威脅。在過去文獻中，許多研究指出實施尖離峰差別訂價能夠有效的改變旅客的行為。因此本研究探討時間彈性相對較高的台灣高鐵休閒旅客出發時間選擇行為的影響因素，透過對於旅客出發時間選擇行為的了解，期望能夠提供台灣高鐵擬定定價策略參考，以設計出符合實際需求且能平衡尖離峰差距的產品。
本研究的研究流程為首先透過敘述性偏好法的問卷設計模擬情境供高鐵旅客選擇，藉此蒐集旅客偏好及基本旅次、社經資訊。再利用蒐集到的數據資料透過個體選擇模式分別建立高鐵旅客去程及回程的出發時間選擇模式。本研究先應用多項羅吉特模式分析對於出發時間選擇行為有顯著影響的社經變數交互項，然而傳統的羅吉特模式並未考慮受訪者不可直接測得的心理因素。因此本研究再利用Hybrid選擇模式納入計畫行為理論(Theory of Planned Behavior, TPB)之潛在變數以將心理因素納入考量，並透過模式結果進行彈性分析以及敏感度分析，最後以情境模擬方式分析應用研究結果後旅客的選擇機率變化。
研究結果顯示高鐵休閒旅客在選擇去程出發時間時會受到延誤時間以及票價的影響，且旅客去程及回程的出發時間選擇行為會互相影響。透過Hybrid選擇模式亦發現旅客的出發時間選擇行為確實會受到心理因素所影響。從彈性分析及敏感度分析可以驗證實施尖離峰差別訂價能夠誘使旅客從尖峰時段轉移至離峰時段搭乘，研究成果可以提供台灣高鐵作為擬訂平衡間離峰策略的參考。

關鍵字：出發時間選擇、Hybrid選擇模式、高速鐵路、休閒旅客

There are a lot of transportation industry have the phenomenon that ridership exists significantly difference between peak hour and off-peak hour, resulting congestion problem in peak hour. In order to induce passengers shift from peak hour to off-peak hour, we need a better understanding in passengers’ departure time choice behavior. This study adopts Taiwan High Speed Rail (THSR) as a case study to identify THSR leisure passengers’ perceptions of the essential factors that affect departure time choice. In the earlier departure time choice relevant literature paid little attention in returning trip. In fact there may be exist differences and association between outgoing trip and returning trip. Therefore this study analysis THSR leisure passengers’ both outgoing trip and returning trip departure time choice behavior. Moreover, this study using hybrid choice model to included psychological factors that affect passengers’ departure time choice behavior. The findings of this study provides empirical evidence that no matter in outgoing trip or returning trip THSR passengers’ departure time choice will be affected by delay time, fares and psychological factors. This study also found that passengers’ departure time choice behavior in outgoing trip and returning trip will affect each other. The conclusions of this study have implication for THSR to design appropriate products that can balance difference between peak hour and off-peak hour.

Key words: Departure Time Choice; Hybrid Choice Model; High Speed Rail; Leisure Passengers

中文文獻
1. 交通部運輸研究所(2015)，103年度中長程計畫審議決策支援系統與整合資料庫維護，臺北市：交通部。
2. 李奇(1992)，敘述性偏好模式與顯示性偏好模式比較之研究，國立成功大學交通管理科學研究所碩士論文。
3. 段良雄、劉慧燕(1996)，敘述性偏好之實驗設計與校估方法，運輸學季刊，25(1)，1-44。
4. 凌瑞賢(2004)，運輸規劃原理與實務，台北市；鼎漢國際工程顧問有限公司。
5. 馮漢昌(2008)，消費者對 3G 電信服務屬性偏好之研究，成功大學電信管理研究所學位論文。
6. 黃歆嵐、張新立(2000)，以旅運者觀點探討高速鐵路車廂選擇行為之研究，國立交通大學運輸工程與管理系碩士學位論文。
7. 鄭永祥、李治綱(2009)，台灣高鐵營收管理模式研發 (一)，中興工程(105)，91-95。
8. 謝文淵(2002)，高鐵高北城際旅客旅次規劃行為之研究，成功大學交通管理科學系學位論文，1-120。

英文文獻
1. Ajzen, I., & Fishbein, M. (1975). A Bayesian analysis of attribution processes.Psychological bulletin, 82(2), 261.
2. Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
3. Arellana, J., Daly, A., Hess, S., de Dios Ortúzar, J., & Rizzi, L. (2012). Development of Surveys for Study of Departure Time Choice: Two-Stage Approach to Efficient Design. Transportation Research Record: Journal of the Transportation Research Board, (2303), 9-18.
4. Ben-Akiva, M. E., & Lerman, S. R. (1985). Discrete choice analysis: theory and application to travel demand (Vol. 9). MIT press.
5. Belobaba, P. (1987). Air travel demand and airline seat inventory management. Cambridge, MA: Flight Transportation Laboratory, Massachusetts Institute of Technology,[1987].
6. Bianchi, R., Jara-Dı́az, S. R., & Ortúzar, J. D. D. (1998). Modelling new pricing strategies for the Santiago Metro. Transport Policy, 5(4), 223-232.
7. Bhat, C. R. (1998). Analysis of travel mode and departure time choice for urban shopping trips. Transportation Research Part B: Methodological, 32(6), 361-371.
8. Ben-Akiva, M., McFadden, D., Gärling, T., Gopinath, D., Walker, J., Bolduc, D., ... & Polydoropoulou, A. (1999). Extended framework for modeling choice behavior. Marketing letters, 10(3), 187-203

9. Ben-Akiva, M., McFadden, D., Train, K., Walker, J., Bhat, C., Bierlaire, M., ... & Daly, A. (2002). Hybrid choice models: progress and challenges. Marketing Letters, 13(3), 163-175.
10. Burris, M. W., & Pendyala, R. M. (2002). Discrete choice models of traveler participation in differential time of day pricing programs. Transport Policy, 9(3), 241-251.
11. Bamberg, S., Rölle, D., & Weber, C. (2003). Does habitual car use not lead to more resistance to change of travel mode?. Transportation, 30(1), 97-108.
12. Bamberg, S., Hunecke, M., & Blöbaum, A. (2007). Social context, personal norms and the use of public transportation: Two field studies. Journal of Environmental Psychology, 27(3), 190-203.
13. Chen, C. D., Fan, Y. W., & Farn, C. K. (2007). Predicting electronic toll collection service adoption: An integration of the technology acceptance model and the theory of planned behavior. Transportation Research Part C: Emerging Technologies, 15(5), 300-311.
14. Chang, J. S., & Lee, J. H. (2008). Accessibility Analysis of Korean High‐speed Rail: A Case Study of the Seoul Metropolitan Area. Transport reviews, 28(1), 87-103.
15. Cheng, Y. H. (2010). High-speed rail in Taiwan: New experience and issues for future development. Transport policy, 17(2), 51-63.
16. Doganis, R. (2002). Flying off course: The economics of international airlines. Psychology Press.
17. Dıaz, E. M. (2002). Theory of planned behavior and pedestrians' intentions to violate traffic regulations. Transportation Research Part F: Traffic Psychology and Behaviour, 5(3), 169-175.
18. De Jong, G., Daly, A., Pieters, M., Vellay, C., Bradley, M., & Hofman, F. (2003). A model for time of day and mode choice using error components logit.Transportation Research Part E: Logistics and Transportation Review, 39(3), 245-268.
19. Fröidh, O. (2005). Market effects of regional high-speed trains on the Svealand line. Journal of transport geography, 13(4), 352-361.
20. Fröidh, O. (2008). Perspectives for a future high-speed train in the Swedish domestic travel market. Journal of Transport Geography, 16(4), 268-277.
21. Givoni, M. (2006). Development and Impact of the Modern High‐speed Train: A Review. Transport reviews, 26(5), 593-611.
22. Hendrickson, C., & Plank, E. (1984). The flexibility of departure times for work trips. Transportation Research Part A: General, 18(1), 25-36.
23. Holguín-Veras, J., Xu, N., Wang, Q., Ozbay, K., Zorrilla, J., & Cetin, M. (2015). New Jersey Turnpike Time-of-Day Pricing Initiative's Behavioral Impacts: Observed Role of Travel Distance on Underlying Elasticities. Transportation Research Record: Journal of the Transportation Research Board.
24. Jou, R. C. (2001). Modeling the impact of pre-trip information on commuter departure time and route choice. Transportation Research Part B: Methodological, 35(10), 887-902.
25. Johansson, M. V., Heldt, T., & Johansson, P. (2006). The effects of attitudes and personality traits on mode choice. Transportation Research Part A: Policy and Practice, 40(6), 507-525.
26. Koppelman, F. S., & Wen, C. H. (2000). The paired combinatorial logit model: properties, estimation and application. Transportation Research Part B: Methodological, 34(2), 75-89.
27. Kamargianni, M., & Polydoropoulou, A. (2013). Hybrid choice model to investigate effects of teenagers' attitudes toward walking and cycling on mode choice behavior. Transportation Research Record: Journal of the Transportation Research Board, (2382), 151-161.
28. Liu, Y., & Charles, P. (2013). Spreading peak demand for urban rail transit through differential fare policy: a review of empirical evidence. In Australasian Transport Research Forum 2013 Proceedings.
29. McFadden, D., & Train, K. (2000). Mixed MNL models for discrete response.Journal of applied Econometrics, 15(5), 447-470.
30. Martín, J. C., & Nombela, G. (2007). Microeconomic impacts of investments in high speed trains in Spain. The Annals of Regional Science, 41(3), 715-733.
31. 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.
32. Nakagawa, D., & Hatoko, M. (2007). Reevaluation of Japanese high-speed rail construction: Recent situation of the north corridor Shinkansen and its way to completion. Transport Policy, 14(2), 150-164.
33. Park, Y., & Ha, H. K. (2006). Analysis of the impact of high-speed railroad service on air transport demand. Transportation Research Part E: Logistics and Transportation Review, 42(2), 95-104.
34. Prato, C. G., Bekhor, S., & Pronello, C. (2012). Latent variables and route choice behavior. Transportation, 39(2), 299-319.
35. Roman, C., Espino, R., & Martin, J. C. (2007). Competition of high-speed train with air transport: The case of Madrid–Barcelona. Journal of Air Transport Management, 13(5), 277-284.
36. Small, K. A. (1987). A discrete choice model for ordered alternatives.Econometrica: Journal of the Econometric Society, 409-424.
37. Temme, D., Paulssen, M., & Dannewald, T. (2008). Incorporating latent variables into discrete choice models—a simultaneous estimation approach using SEM software. BuR-Business Research, 1(2), 220-237.
38. Vishnu, B., & Srinivasan, K. K. (2013). Tour-based Departure Time Models for Work and Non-work Tours of Workers. Procedia-Social and Behavioral Sciences, 104, 630-639.
39. Weatherford, L. R., & Bodily, S. E. (1992). A taxonomy and research overview of perishable-asset revenue management: yield management, overbooking, and pricing. Operations Research, 40(5), 831-844.
40. Westaby, J. D., Probst, T. M., & Lee, B. C. (2010). Leadership decision-making: A behavioral reasoning theory analysis. The Leadership Quarterly,21(3), 481-495.
41. Yang, C. W., & Chang, C. C. (2011). Applying price and time differentiation to modeling cabin choice in high-speed rail. Transportation Research Part E: Logistics and Transportation Review, 47(1), 73-84.