需求預測對於國道客運公司的日常管理工作非常重要，可作為調整服務水準以及人員調度的參考依據。若能及時了解並區隔消費者的需求特性，業者將可更有效地透過不同市場定價方式以追求極大化收益。為達上述目標，國道客運公司必須有能力整合需求預測以及票價制定的工作，始能建立國道客運業者營收管理機制。
本研究分析國內一家國道客運公司的售票資料，根據每位顧客在預約系統中的每筆訂票紀錄資訊來建立期望銷售曲線，並運用簡單迴歸、K個鄰近樣本法以及加法型增量法模式來預測發車日尖峰單位兩小時內的銷售量，俾以建立國道客運需求預測模型的工具庫。其次，進一步探討敘述性偏好模式來測知旅客對於不同票種限制的願付價格，以做為國道客運業者制定運輸服務票價結構時的參考。最終，本研究考量不同的票種限制條件並搭配相對應的票價折扣，設計出數種符合市場旅客偏好的票種。情境試算結果發現在需求彈性較大的出發時段實施多元化票種結構，將有助於提升營收。
本研究搭配需求預測模式及票價折扣與票種限制等課題，由客運業者角度切入主題，研究成果可做為國道客運公司在調整服務班次及擬定票價促銷計畫時的參考。整體來說，當預測未來某一時段內的需求低迷時，應採用有票種限制的折扣票，避免一體適用的降低價格方案導致營收受到侵蝕。

Peak service demand forecasting is a crucial management activity for service providers since the forecast results can be used as the base for daily operations such as schedule adjustment and service level improvement. On the other hand, corporations can allocate their resources efficiently and effectively to pursue maximized revenue if they are capable of segmenting the mass market.
This study researches on historical reservation data of an intercity bus corporation in Taiwan for constructing different types of demand curves and focuses on one specific curve pattern named expected sales curves. Regression, k nearest neighbor and additive pick-up models are utilized to construct forecasting models and compare with each other to understand the predicting capability of the models in the study case. Secondly, this study focuses on consumer preferences for fare structure which is the combination of discounts and user restrictions. Willingness-to-pay of each restriction is further calculated to form the base of a pricing menu. Thirdly, we consider various restrictions with discounts to design a market-oriented price menu. The simulation shows that implying the pricing menu results in obtaining extra revenues when price elasticity is high.
Predicting demand and understanding customer preferences may help the intercity bus corporation conduct suitable daily operational plans and further create opportunities to increase revenues. This study verifies the benefit of the above two capabilities. We also provide managerial applications at the end of this paper.

1. 王穆衡等 (2007)，「高鐵通車後國內旅客運輸業因應策略與政府輔導措施之研究」，交通部運輸研究所。
2. 李佳瑩 (2006)，「探討在收益管理架構下城際軌道運輸系統訂位選擇行為之研究」，國立高雄第一科技大學運輸倉儲營運所碩士論文。
3. 李香怡 (2004)，「時間差別定價對台北捷運乘客旅運行為之影響」，國立交通大學交通運輸研究所碩士論文。
4. 洪振富 (2007)，「民眾對運輸需求管理策略偏好意向之研究－以大高雄地區為例」，國立成功大學交通管理學系碩士論文。
5. 姜榮新 (1999)，「捷運系統票價彈性之研究」，國立交通大學交通運輸研究所碩士論文。
6. 陳淑佩 (2008)，「列車服務訂票曲線之預測分析」，國立成功大學交通管理科學研究所碩士論文。
7. 雷明峰 (2002)，「航空業機位配置動態定價模型之研究」，國立臺灣大學商學研究所碩士論文。
8. 蔡宗憲、李治綱、游智元 (2008)，「模式更新與資料轉換對時間序列方法於短期列車旅運需求預測之影響」，運輸學刊，第20卷，第2期，頁179-200。
9. 蔡宜紜 (2011)，「國際航線機票動態定價之研究」，逢甲大學運輸科技與管理科學研究所碩士論文。
10. 蘇霜吉 (2006)，「列車服務與票價整合之模式-以台灣高鐵為例」，國立成功大學交通管理科學研究所碩士論文。
11. Belobaba, P. P. and Wilson, J. L. (1997) “Impacts of Yield Management in Competitive Airline Markets, ” Journal of Air Transport Management, Vol. 3, No. 1, pp. 3-9.
12. Chu, F. L. (2009) “Forecasting Tourism Demand with ARMA-based Methods,” Tourism Management, Vol. 30, No. 5, pp. 740-751.
13. Engel, J. F., Blackwell, R. D. and Miniard, P. W. (1993) “Consumer Behavior,” 7th ed., Orlando Florida, Dryden Press.
14. Engel, J.F., Kollat, D.T. and Blackwell, R.D. (1982) “Consumber Behavior,” 4th ed., Taipei: Hwa-Tai Co..
15. Fowkes, T. and Preston, J. (1991) “Novel Approaches to Forecasting the Demand for New Local Rail Service,” Transportation Research A, Vol. 25A, pp. 209-218.
16. Fowkes, T. and Wardman, M. (1988) “The Design of Stated Preference Travel Choice Experiments,” Journal of Transport Economics and Policy, Vol. 22, No.1, pp. 27-44.
17. Harris, A.E. et al. (1999) “Metropolitan Atlanta Rapid Transit Authority Fare Elasticity Model,” Transportation Research Record, No. 1669, pp. 123-128.
18. Hendrickson, C. and Plank, E. (1984) “The Flexibility of Departure Times for Work Trips,” Transportation Research Part A, Vol. 18A, No. 1, pp. 25-36.
19. Hyndman, R. (2010) “Another Look at Forecast-Accuracy Metrics for Intermittent Demand, In Forecast Accuracy Measurement: Pitfalls to Avoid and Practices to Adopt,” International Institute of Forecasters, U. S. A..
20. Kimes, S. E. (1989) “Yield Management: A Tool for Capacity-Constrained Service Firms,” Journal of Operations Management, Vol. 8, No. 4, pp. 348-363.
21. Kolter, P. (1998) “Marketing Management: Analysis, Planning, Implementation and Control,” 9th ed., Prentic-Hall Inc..
22. Lee, A. O. (1990) “Airline Reservations Forecasting: Probabilistic and Statistical Models of the Booking Process,” Flight Transportation Laboratory Report 90-5, Massachusetts Institute of Technology.
23. Lim, C., Chanf, C. and McAleer, M. (2009) “Forecasting H(m)otel Guest Nights in New Zealand,” International Journal of Hospitality Management, Vol. 29, pp. 228-235.
24. Loudon, D. and Bitta, A. J. (1988) “Why Study Consumer Behavior,” Consumer Behavior-Concept and Application, 3rd ed., Singapore.
25. Marka, T.L. and Swait, J. (2004) “Using Stated Preference and Revealed Preference Modeling to Evaluate Prescribing Decisions,” Health Economics, Vol.13, pp. 563-573.
26. Pearmain, D. (1991) “Stated Preference Techniques: A Guide to Practice,” Second Edition, New York: Steer Davies Gleave and Hague Consulting Group.
27. Philip, K. (2005) “Marketing Management, Hardcover,” 12th Edition, 816 pages.
28. Rajopadhye, M. et al. (2001) “Forecasting Uncertain Hotel Room Demand,” Information Sciences, Vol. 132, pp. 1-11.
29. Ryan, S. and Wordsworth, S. (2000) “Sensitivity of Willingness to Pay Estimates to The Level of Attributes in Discrete Choice Experiments,” Journal of Political Economy, Vol. 47, No. 5, pp. 504-524.
30. Rannou, B. and Melli, D. (2003) “Measuring the Impact of Revenue Management,” Journal of Revenue and Pricing Management, ABI/INFORM Global, Vol. 2, No. 3, pp. 261-270.
31. Ridel, S. and Gabrys, B. (2007) “Combination of Multi Level Forecasts,” Journal of VLSI Signal Processing, Vol. 49, pp. 265-280.
32. Robinson, S. and Polak J. W. (2007) “Characterizing Components of Urban Travel Time Variability Using K-Nearest-Neighbor Method,” Proceedings of the 86th TRB meeting, Washington.
33. Schiffman, L. G. and Kanuk, L. L. (1991) “Consumer Behavior,” Englewood Cliffs, New Jersey, Prentice-Hall Inc..
34. Segura, J. V. and Vercher, E. (2001) “A Spreadsheet Modeling Approach to the Holt-Winters Optimal Forecasting,” European Journal of Operational Research, Vol. 131, pp. 375-388.
35. Sfetsos, A. and Siriopoulos, C. (2004) “Combinatorial Time Series Forecasting Based on Clustering Algorithms and Neural Networks,” Neural Computation & Application, Vol. 13, pp. 56-64.
36. Shen, H. and Huang, J. Z. (2008) “Interday Forecasting and Intraday Updating of Call Center Arrivals,” Manufacturing and Service Operations Management, Vol. 10, No. 3, pp. 391-410.
37. Taylor, W. J. and Kimes, S. E. (2010) “How Hotel Guests Perceive the Fairness of Differential Room Pricing,” Cornell Hospitality Report, Vol. 10, No. 2, pp. 6-13.
38. Taplin, J. D., Hensher, and Smith, B. (1999) “Preserving the Symmetry of Estimated Commuter Travel Elasticities,” Transportation Research Part B- Methodological, Vol.33, No.3, pp. 215–232.
39. Tsai, T. H. (2009) “A Temporal Case Retrieval Model to Predict Railway Passenger Arrivals,” Expert Systems with Applications, Vol. 36, No. 5, pp. 8876-8882.
40. Weatherford, L. R. and Kimes, S. E. (2003) “A Comparison of Forecasting Methods for Hotel Revenue Management,” International Journal of Forecasting, Vol. 19, pp. 401-415.
41. Weatherford, L. R., Gentry, T. W. and Wilamowski, B. (2003) “Neural Network Forecasting for Airlines: A Comparative Analysis,” Journal of Revenue and Pricing Management, Vol. 1, No. 4, pp. 319-331.
42. Wen, C.H. (2009) “Mixed Logit Analysis of International Airline Choice,” Journal of the Transportation Research Board, No. 2106, pp. 20-29.
43. Wickham, R. R. (1995) “Evaluation of Forecasting Techniques for Short-Term Demand of Air Transportation,” Master thesis, MIT Press.
44. Williams, B. M., Durvasula, P. K. and Brown, D. E. (1998) “Urban Freeway Traffic Flow Predictionl: Application of Seasonal Autoregressive Integrated Moving Average and Exponential Smoothing Model,” Transportation Research Record, No.1644, pp. 132-141.
45. Wong, K. et al. (2007) “Tourism Forecasting: To Combine or not to Combine,” Tourism Management, Vol. 28, pp. 1068-1078.
46. Yoo, S.H., Kwak S.J. and Lee, J.S. (2008) “Using A Choice Experiment to Measure The Environmental Cost of Air Pollution Impacts In Seoul,” Journal of Environment Management, Vol.86, No. 1, pp. 308-318.