在全球永續運輸思潮推動下，政府積極鼓勵民眾使用大眾運輸工具，但國人普遍偏好在國道使用私人運具，導致國道客運使用率遠低於其他大眾運輸。因此，如何提出適合且有效的運輸行銷策略，提升民眾搭乘意願，並減輕對環境的衝擊影響，已成為重要的研究課題。傳統上，大眾運輸行銷多以提升服務品質或降低票價來吸引民眾搭乘。而服務品質研究大多著眼於發掘並改善旅客不滿意的服務，雖可保有舊有客源，但對於拓展客源的效果有限。研究顯示，有效的大眾運輸行銷需要探索民眾對於大眾運輸的態度及潛在需求，並導入創新服務思維以滿足消費者期待，針對各族群提出不同的行銷策略。自80年代起，學者陸續提出不同的發展創新服務操作流程，但往往流於概念式的架構，缺乏可行的操作方法。因此，本研究彙整學者所提出之各類型創新服務發展架構，提出一個結合創新服務與運輸行銷的基本研究架構。
　　本研究建構以感知-意向-適應模型為基礎的態度問卷，透過隨機森林法進行市場區隔，探討不同族群對於國道客運之態度以及潛在需求，並據以建立分群預測模型。其後，與一般民眾進行半開放式問卷訪談，問卷結果會根據預測模型進行分類，將受訪者分配至相對應族群，探討各族群旅客的期望服務。再與國道客運業者討論各族群期望新服務的可行性，針對各族群擬定可行的行銷策略，並探討業者與民眾之間對於期望服務的差異及原因。
　　研究結果顯示，完整且有效的行銷策略需結合公、私部門共同投入，建立短、中及長期的整合計畫。短期行銷策略應著重於各族群皆期望且業者認為可立即提供同時可行性也較高的服務，如提供觀光景點一日券、景點直達車以及提供整合性的國道客運與觀光資源應用軟體等。中期行銷策略可依照業者所欲行銷的目標族群，提供該族群期望的新服務;同時，透過公部門的推動，逐步開放電子票證於國道客運上使用。長期的行銷策略則是以達成政府所提倡空間、時間、資訊及服務無縫的概念為目標，透過與公部門的合作，逐步建設相關的完整軟硬體基礎設施，提供更為便利的運輸環境。

Due to the global trend of sustainable transportation, the government proactively encourages general public to use transit. However, most of people still prefer to use private vehicles on highway which significantly affect the ridership of inter-city transit. Therefore, it is important to develop suitable and efficient marketing strategies to increase ridership and mitigate the environmental impacts.  Traditionally, the marketing strategies of transit mainly focus on reducing the fare or providing proper service quality to attract passengers. Using service quality research to explore unsatisfied services and then adjust them could increase the satisfaction level but it is difficult to attract new passengers. Study shows that to understand public attitude toward transit and latent demand is critical to develop effective strategies.  Providing a variety of new services for each target groups to fulfill their expectation through market segmentation is the best way to attract more passengers.  Starting from 1980s, scholars have been proposing many types of new service development process; however, they tend to provide a conceptual system lacking operational framework.  Therefore, we combine previous research results to develop a simple operational framework for integrating new service development and transit marketing in this research.
　　This research first develops an attitude questionnaire based on Perception-Intention-Adaptive model and then uses random forest to conduct market segmentation in order to explore public attitude and latent demand for each group.  Also, a prediction model is built based on the collected data.  Second, we use the same questionnaire with open-ended questions to collect expected new services from public and use the prediction model to assign expectation to each target groups.Finally, the feasibility of expected new services is screened by the industrial experts and the differences between public and industrial experts are also discussed.  
　　The study results show that integrating the efforts of government and transit industry to develop a series of comprehensive plans can build effective marketing strategies.  Short term marketing strategies should focus on providing services that fulfills the expectation of all groups and also can be provided immediately by the industry, such as express services to each scenic site and full function integrated smartphone application.  The midterm marketing strategies are to provide new services to targeted groups and allow the use of electronic payment.  The long term marketing strategies are to achieve the goal of seamless transportation which is advocated by government.  The cooperation of the government and the industry can provide an attractive and convenient transportation system.

目錄
第一章、緒論	1
1.1研究背景與動機	1
1.2研究目的	5
1.3研究範圍與限制	5
1.4研究流程圖	6
二、文獻回顧	7
2.1國道五號現況	7
2.2行銷及市場區隔	9
2.3服務的定義	12
2.4服務品質之定義	16
2.4.1國道客運服務品質相關文獻	18
2.5創新服務發展	19
2.5.1創新服務發展流程及工具	22
2.5.2創新服務發展相關文獻	26
2.6旅客態度及潛在需求	29
2.7感知-意向-適應模式	33
2.8隨機森林	34
2.8.1 隨機森林之優點	37
2.9文獻小結	38
三、研究方法	42
3.1研究架構	42
3.2問卷內容	44
3.2.1問卷設計與尺度衡量	44
3.2.2問卷調查	46
3.2.3問卷設計內容	46
3.3研究方法	48
3.3.1隨機森林	48
3.3.2分類迴歸樹	49
3.3.3取後放回及袋外資料	51
3.3.4 隨機森林建構流程及準確性	52
3.3.5隨機森林最佳化建模	54
3.4訪談	55
3.5小結	56
四、資料分析與結果	57
4.1旅客PIA架構問卷資料分析	58
4.1.1樣本結構	58
4.1.2敘述統計分析	65
4.1.3信度分析	67
4.1.4效度分析	69
4.2隨機森林模型	69
4.2.1 集群分析	70
4.3集群分析小結	78
4.4旅客訪談	80
4.4.1旅客訪談結果	82
4.4.2客運愛好者	82
4.4.3潛在使用者	84
4.4.4次要潛在使用者	86
4.4.5客運排斥者	87
4.5業者訪談	89
4.6差異性分析	93
4.7 小結	94
五、結論與建議	95
5.1結論	96
5.2建議	99
六、參考文獻	101
附錄一-PIA態度問卷	111
附錄二-訪談問卷	114

 
圖目錄
圖1.1 研究流程圖	6
圖2.1 行銷流程圖	10
圖2.2 創新服務發展之架構比較	25
圖2.3創新服務發展期望產出	26
圖2.4創新服務發展流程圖	28
圖2.5 理性行為理論	32
圖2.6 計畫行為理論	33
圖2.7 態度-意向-適應模型	33
圖2.8 傳統之服務品質研究	39
圖3.1 研究流程圖	43
圖3.2分類及迴歸樹	50
圖3.3 資料過度擬合	51
圖3.4 隨機森林建模與預測之步驟	54
圖3.5 最佳化隨機森林模型建構流程	55
圖4.1 決策樹數量與模型誤差關係圖	70

 
表目錄
表2.1 宜蘭政府舉辦之活動	8
表2.2 服務之定義	13
表2.3 服務品質之定義	17
表2.4 新服務之定義	21
表2.5 態度導向問卷	31
表2.6 隨機森林分群相關文獻	37
表3-1 旅客對國道客運態度之構面與準則	45
表4.1 態度問卷性別分配	58
表4.2 態度問卷年齡分配	59
表4.3 態度問卷學歷分配	59
表4.4 態度問卷收入分配	60
表4.5 駕照持有數	60
表4.6 此次旅行住宿與否	61
表4.7 此次旅行目的	61
表4.8 國道客運是旅次時主要的運輸工具	62
表4.9 未來的旅次會更常搭乘國道客運	62
表4.10 旅次中使用國道客運是簡單的一件事	63
表4.11 同行人數	63
表4.12 旅行成員	64
表4.13 搭乘運輸工具分配	64
表4.14 居住地	64
表4.15 對於國道客運之態度平均數及標準差	66
表4.16 對於服務的看法平均數及標準差	66
表4.17 對於時間及價格的看法平均數及標準差	67
表4.18社會與個人規範平均數及標準差	67
表4.19各準則Cronbach’ Alpha值	68
表4.20市場區隔下各族群基本資料	73
表4.21態度問卷市場區隔	75
表4.22不同族群對於國道客運服務看法	76
表4.23不同族群對於國道客運價格以及時間的看法	77
表4.24不同族群對於社會以及個人規範的看法	78
表4.25隨機森林模型預測結果	82
表4.26可行性較高之服務	90
表4.27可行性較低之服務	92
表4.28必要性較低之服務	92

中文部分
1.	巫天虹（2013），以兩階段分類法建構信用卡授信決策模型的實務評估，淡江大學統計學系應用統計學碩士班碩士論文。
2.	李靜芳、陳耀茂、林寒琦、鍾健平（2012），企劃國道客運業新型服務之實證研究，明道學術論壇，6(4)，3-21。
3.	林軒正（2002），改善國道客運業服務品質缺口之研究，國立交通大學運輸科技與管理學系碩士論文。
4.	林伯勳（2013），使用隨機森林演算法做煙火偵測，國立暨南大學資訊工程學系研究所碩士論文。
5.	周依蓉（2009），新服務發展-方法論的探討與分類，國立台灣大學管理學院資訊管理學研究所碩士論文。
6.	周建張、藍雅莉、莊麗秋、吳佩穎（2005），國光客運與統聯客運服務品質之比較，都市交通季刊，20(1)，33-44。
7.	周春芳（2006），創新服務行銷：開拓藍海商機 ，五南圖書出版公司。
8.	徐淵靜、陳菀蕙（2006），高齡社會研究規劃成果報告書－高齡社會的來臨：為2025台灣社會規劃之整合研究（交通與運輸組），行政院國家科學委員會。
9.	許銓倫（2001），高齡者交通特性與交通設施之檢討，國立交通大學交通運輸研究所碩士論文。
10.	郭建良（2008），產業觀點下的服務創新方法論V1.0，工業技術研究院產業經濟與趨勢研究中心。
11.	陳宏佳（2013），國道客運服務品質缺失辨識與改善策略-以H國道客運公司為例，中華大學企業管理學系碩士論文。
12.	陳明鴻（2012），針對「創新服務概念發展」之設計程序，國立成功大學工業設計學系碩博士班。 
13.	陳景祥（2010），R軟體-應用統計方法，東華書局。
14.	黃文、王正林(2015) ，利用R語言打通大數據的經脈，臺北市：佳魁資訊。
15.	黃兆鉑（2005），高齡社會大眾運輸課題與改善策略之研究－以大台北都會區為例，國立交通大學交通運輸研究所碩士論文。
16.	黃維鈞（2007），利用P7改善連鎖藥局經營方式創造顧客價值之研究，東海大學企業管理研究所碩士論文。
17.	楊錦洲（2009），服務品質－學理到應用，華泰出版。
18.	潘婉茹（2008），結合KANO模式與IPA檢視國道客運之服務品質屬性-以國光客運為例，國立彰化師範大學行銷與物流管理研究所論文。
19.	劉純強（2004），校園智慧卡整合運輸電子票證之規劃研究－以燕巢大學城為例，樹德科技大學資訊管理研究所碩士論文。
20.	鍾健平、李靜芳、陳耀茂、吳東洋（2012），以商品企劃七工具及約略集合分析企劃計程車新型服務，長庚人文社會學報。

英文部分
1.	Ajzen, I. (1985), “From Intentions to Action: A Theory of Planned Behaviour”, In Kuhl, J. and Beckmann, J. (Eds.), Action-control: From cognition to behavior, Heidelberg: Springer-Verlag, 11-39.
2.	Alam, M. S. and Vuong, S. T. (2013), “Random Forest Classification for Detecting Android Malware”, Physical and Social Computing, IEEE, 663-669.
3.	Anable, J. (2005), “Complacent Car Addicts or Aspiring Environmentalists? Identifying Travel Behaviour Segments using Attitude Theory”, Transport Policy, 12(1), 65-78.
4.	Baharun, R. and Feiz, S. (2012), “A Review of Service Quality Models”, 2nd international conference on management.
5.	Barinova, O., Shapovalov, R., Sudakov, S. and Velizhev, A. (2012), “Online Random Forest for Interactive Image Segmentation”, EEML 2012–Experimental Economics in Machine Learning, 1.
6.	Bessant, J., Lamming, R., Noke, H. and Phillips, W. (2005), “Managing Innovation Beyond the Steady State”, Technovation, 25(12), 1366-1376.
7.	Bettencourt, L. A., Brown, S. W. and Sirianni, N. J. (2013), “The Secret to True Service Innovation”, Business Horizons, 56(1), 13-22. 
8.	Breiman, L. (2001), “Random Forests”, Machine Learning, 45(1), 5-32.
9.	Carbonell, P., Rodrigyez-Escudero, A. I. and Pujari, D. (2012), “Performance Effects of Involving Lead User and Close Customers in New Service Development”, Journal of Marketing, 26(7), 497-509.　
10.	Chen, S. H., Wen, P. C. and Yang, C. K. (2014), “Business Concepts of Systemic Service Innovations in E-Healthcare”, Technovation, 34(9), 513-524.
11.	Collum, K. K. and Daigle, J. J. (2015), “Combining Attitude Theory and Segmentation Analysis to Understand Travel Mode Choice at a National Park”, Journal of Outdoor Recreation and Tourism, 9, 17-25.
12.	Dayu, J., Hin, C. K. and Chuan, T. K. (2010), “A Conceptual Framework on the Adoption of New Service Development Tools and Techniques in Service Firms”, In Technology Management for Global Economic Growth (PICMET), 1-9.
13.	Fishbein, M. and Ajzen, I. (1975), Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research, Reading, MA: Addison-Wesley.
14.	Fitzsimmons, J. and Fitzssimmons, M. J. (2000), “New Service Development: Creating Memorable Experiences”, SAGE Publications Inc, 18-19.
15.	Fried M., Havens J. and Thall, M. (1977), “Travel Behavior-A Synthesized Theory”, Final Report. NCHRP, Transportation Research Board, Washington, DC, 8-14.
16.	Fu, X. and Juan, Z. (2015), “Transit Commuting Market Investigation using the Latent Segmentation Approach”, Travel Behaviour and Society, 2(2), 102-108.
17.	Grönroos, C. (1982), “An Applied Service Marketing Theory”, European Journal of Marketing, 16(7), 30-41.
18.	Goyat, S. (2011), “The Basis of Market Segmentation: A Critical Review of Literature”, European Journal of Business and Management, 3(9), 45-54.
19.	Gustafsson, A. and Johnson, M. (2006), “Competing in a Service Economy”, John Wiley and Sons.
20.	Harvey, J. (1998), “Service Quality: A Tutorial”, Journal of Operations Management, 16(5), 583-597.
21.	Havens, J. (1981), “New Approach to Understanding Travel Behavior : Role , Life-Style, and Adaption ”, In: Stopher, P., Meyburg, A., Brög, W. (Eds.),New Horizon in Travel-Behavior Research, LexingtonBooks, Lextington, Massachusetts,  269-287.
22.	Haywood-Farmer, J. (1988), “A Conceptual Model of Service Quality”, International Journal of Operations and Production Management, 8(6), 19-29.
23.	Hiziroglu, A. (2013), “Soft Computing Applications in Customer Segmentation: State-of-art review and critique”, Expert Systems with Applications, 40(16), 6491-6507.
24.	Ho, T. K. (1995), “Random Decision Forest”, Proc. of the 3rd Int'l Conf. on Document Analysis and Recognition, Montreal, Canada, 278-282.
25.	Hunecke, M., Haustein, S., Böhler, S. and Grischkat, S. (2010), “Attitude-Based Target Groups to Reduce the Ecological Impact of Daily Mobility Behavior”, Environment and Behavior, 42(1), 3-43.
26.	Iverson, L. R., Prasad, A. M., Matthews, S. N. and Peters, M. (2008), “Estimating Potential Habitat for 134 Eastern US Tree Species Under Six Climate scenarios”, Forest Ecology and Management, 254(3), 390-406.
27.	Jain, A. K. (2010), “Data Clustering: 50 Years Beyond K-Means”, Pattern recognition letters, 31(8), 651-666.
28.	Jiang, K. (2008), “New Service Development for Interactive Experience”, Service Operations and Logistics, and Informatics, IEEE/SOLI 2008, IEEE International Conference on, IEEE, 1, 10-14.
29.	Jin, D., Chai, K. H. and Tan, K. C. (2010), “A Conceptual Framework on the Adoption of New Service Development Tools and Techniques in Service Firms”, Technology Management for Global Economic Growth (PICMET), 2010 Proceedings of PICMET '10, IEEE, 1-9.
30.	Jin, M., Govindarjan, L.N. and Chang, L. (2014), “A Random-Forest Random Field Approach for Cellular Image Segmentation”, Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium on, IEEE, 1251-1254.
31.	Johne, A. and Storey, C. (1997), “New Service Development: A Review of the Literature and Annotated Bibliography”, European Journal of Marketing, 32(3/4), 187-188.
32.	Johnson, S. P., Menor, L. J., Roth, A. V. and Chase, R. B. (2000), “A Critical Evaluation of the New Service Development Process: Integrating Service Innovation and Service Design”, In: Fitzsimmons, J.A., Fitzsimmons, M.J. (Eds.), New Service Development—Creating Memorable Experiences. Sage Publications, Thousand Oaks, CA, 1–32.
33.	Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R. and Wu, A. Y. (2002), “An Efficient K-Means Clustering Algorithm: Analysis and Implementation”, Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(7), 881-892.
34.	Kelly, D. and Storey, C. (2000), “New Service Development: Initiation Strategies”, Library Consortium Management: An International Journal, 2(5/6), 104-122.
35.	Kelly, S.W. (1992), “Developing Customer Orientation Among Service Employees”, Journey of the academy of marketing science, 20, 27-36.
36.	Khan, R., Hanbury, A. and Stoettinger, J. (2010), “Skin Detection: A Random Forest Approach”, Image Processing (ICIP), 2010 17th IEEE International Conference on IEEE, IEEE, 4613-4616.
37.	Kolter, P. (1991), “Marketing Management: Analysis“, planning and control (7th ed.), N. J.: Pentice Hall.
38.	Kolter, P. and Armstrong, G. (1996), “Principles of Marketing (7th ed.)”, NJ: Pentice Hall.
39.	Kotler, P. and Keller, K. L. (2011), “Marketing Management (14th edition)”, Pearson. 
40.	Lee, C., Lee, H., Seol, H. and Park, Y. (2012), “Evaluation of New Service Concepts using Rough Set Theory and Group Analytic Hierarchy Process”, Expert Systems with Applications, 39(3), 3404-3412.
41.	Lewis, R. C. and Booms, B. (1983), “The Marketing Aspects of Service Quality”, AMA Proceeding, American Marketing Association Chicago, 99-104.
42.	Lo, H. P., Chiu, P. L. and Chiu M. C. (2013), “A Systematic Methodology for Service Innovation and Performance Measurement with a Home Service Case Study”, Service Science and Innovation (ICSSI), 2013 Fifth International Conference on, IEEE, 162-169.
43.	Lovelock, C. H. and Wirtz, J. (2016), “Services Marketing: People, Technology, Strategy (7th edition)”, Prentice Hall.
44.	Lundkvist, A. and Yakhlef, A. (2004), “Customer Involvement in New Service Development: A Conversational Approach”, Managing service quality, 14(2/3), 249-257.
45.	Madden, T. J., Ellen, P. S. and Ajzen, I. (1992), “A Comparison of the Theory of Planned Behavior and the Theory of Reasoned Action”, Personality and social psychology Bulletin, 18(1), 3-9.
46.	Mei, J., He, D., Harley, R. and Habetler, T. (2014), “A Random Forest Method for Real-Time Price Forecasting in New York Electricity Market”, PES General Meeting | Conference & Exposition, IEEE, 1-5.　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　
47.	Parasuraman, A., Zeithaml, V. A. and Berry, L. L. (1985), “A Conceptual Model of Service Quality and Its Implications for Future Research”, the Journal of Marketing, 41-50.
48.	Perbet, F., Stenger, B. and Maki, A. (2009), “Random Forest Clustering and Application to Video Segmentation”, In BMVC, 1-10.
49.	Pilat, D. (2000), “No Longer Services as Usual”, The OECD Observer 223, 52–54.
50.	Redmond, L. (2000), “Identifying and Analysis Travel-Related Attitudinal, Personality, and Lifestyle Clusters in the San Francisco Bay Area”, Institute of transportation studies.
51.	Ronney, E., Olfe, P. and Mazur, G. (2000), “Gemba Research in the Japanese Cellular Phone Market”, In Proc. 12th Symp. Quality Function Deployment (p. 358374).
52.	Santner, J., Unger, M., Pock, T., Leistner, C., Saffari, A. and Bischof, H. (2009), “Interactive Texture Segmentation using Random Forests and Total Variation”, In BMVC , 1-12.
53.	Sasser, W. E., Olsen, R. P. and Wyckoff, D. D. (1978), “Management of Service Operations: Text Cases, and Readings”, Allyn and Bacon, Boston.
54.	Scheuing, E. E. and Johnson, E. M. (1989), “A Proposed Model for New Service Development”, Journal of service marketing, 3(2), 25-34.
55.	Schroff, F., Criminisi, A. and Zisserman, A. (2008), “Object Class Segmentation using Random Forests”, In BMVC, 1-10.
56.	Shiftan, Y., Outwater, M. L. and Zhou, Y. (2008), “Transit Market Research using Structural Equation Modeling and Attitudinal Market Segmentation”, Transport Policy, 15(3), 186-195.
57.	Smith, W. R. (1956), “Product Differentiation and Market Segmentation as an Alternative Marketing Strategy”, Journal of marketing, 21, 3-8.
58.	Spears, S., Houston, D. and Boarnet, M. G. (2013), Illuminating the Unseen in Transit Use: A Framework for Examining the Effect of Attitudes and Perceptions on Travel Behavior, Transportation Research Part A: Policy and Practice, 58(C), 40-53.
59.	Tatikonda, M.V. and Zeithaml, V.A. (2002), “Managing the New Service Development Process: Multi-Disciplinary Literature Synthesis and Directions for Future Research”, New Direction in Supply-Chain management, New York: AMACDM.
60.	Thogersen, J. (2006), “Understanding Repetitive Travel Mode Choices in a Stable Ontext: A Panel Study Approach”, Transp. Res. A: Policy Pract, 40(8), 621–638.
61.	Thomas, J.W. (2007), “Market Segmentation”, Decision annlyst.
62.	Verikas, A., Gelzinis, A. and Bacauskiene, M. (2011), “Mining Data with Random Forests: A Survey and Results of New Tests”, Pattern Recognition, 44(2), 330-349.
63.	Wagstaff, K., Cardie, C., Rogers, S. and Schrödl, S. (2001), “Constrained K-Means Clustering with Background Knowledge“, ICML, 1, 577-584.
64.	Xu, L., Li, J.H., Liu, X.(2008), “New Service Development using Gap-Based Quality Deploument: Hall Service of Mobile Telecommunication case“, 2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEE, 643-649. 
65.	Yi, Z. and Pan, J. (2010), “Application of Random Forest to Stellar spectral Classification”, Image and Signal Processing (CISP), 2010 3rd International Congress on, 7, IEEE, 3129-3132.
66.	Zeithaml, V. A. and Bitner, M. J. (2000), “Services Marketing: Integrating Customer Focus Across the Firm (2th ed.)”, London: McGraw-Hill.

網頁檢索
1.	蘭華旅遊網-http://jsy.myweb.hinet.net/fordwon.htm
2.	運輸研究所-http://www.iot.gov.tw/mp.asp?mp=1
3.	經濟部電子報http://itriexpress.blogspot.tw/2012/11/blog-post_6686.html
4.	大紀元－節省時間，掃墓公車提供服務
http://www.epochtimes.com/b5/14/4/4/n4123512.htm%E7%AF%80%E7%9C%81%E6%99%82%E9%96%93--%E6%8E%83%E5%A2%93%E5%85%AC%E8%BB%8A%E6%8F%90%E4%BE%9B%E6%9C%8D%E5%8B%99.html