隨著自駕科技的技術逐步成熟，我國政府正積極推動自駕運具的測試營運，盼望透過自駕巴士的服務來填補現今交通運輸系統的服務缺口。過去針對自駕運具的研究大多探討使用者接受程度和知覺價值，較少以服務屬性的變化探討使用者是否會願意選擇該項服務項目。此外文獻也常以通勤族群的使用需求進行討論，缺乏以觀光產業或是觀光客的運輸需求的研究課題，且兩族群對於交通運輸的需求特性不同。綜合上述因素，本研究使用敘述性偏好法對498位受訪者調查觀光地區使用自駕巴士服務屬性偏好。利用個體選擇模式中的多項羅吉特模式瞭解受訪者對於服務屬性的偏好，同時探討個人異質性相關的潛在心理變數對選擇行為的影響，建構了結合選擇模型與潛在變數的ICLV模式。
研究結果指出受訪者最在意智慧導覽功能，參酌國內外的觀光巴士的現況，在旅遊中給予相關的旅遊資訊可以帶給使用者更多的便捷性，讓使用者在車內時間能主動接收到旅遊景點資訊，提升景點的吸引力。此外受訪者也關心自駕巴士行駛的道路類型，考量我國道路環境較為複雜，目前多數的測試計畫案都是在專用道或是非混和車流的道路環境下進行測試，未來推行自駕巴士商業運行時更需要衡量道路環境對使用者的影響。本研究納入的潛在變數中，發現新旅遊方式的樂趣、享樂性皆對選擇效用產生正向顯著影響，代表在當旅遊中提供更多的樂趣性將會提升選擇自駕巴士的效用，未來推行商業化營運時可以更加留意。比較多項羅吉特模式和納入潛在變數的ICLV模式，皆可以發現ICLV模型適配程度更好，代表在衡量使用者選擇自駕巴士服務方案時，除了考慮方案屬性和人口統計變數外，也需要考慮個人潛在心理變數，才能夠貼近使用者實際的選擇行為。

With the advancement of autonomous technology, in the past studies, they widely explored the user acceptance and perceived values. However, few about exploring the factors whether people adopt autonomous bus service through the attribute contained in the service. This study uses the stated preference method to conduct a survey of 498 people for autonomous bus service. Using multinomial logit model of discrete choice model to understand people preference for service attribute and explore the influence of latent psychological factors of individual heterogeneity on choice preference. Thus, establishing an ICLV model that combines choice model sand latent variable. Result shows that people are concerned the smart guilding services. The operators can provide more tourism information to increase the convenience and utility while in-vehicle time. The second noticed attribute is the type of road on which the autonomous bus travel. In the future, when the commercial operation of autonomous bus is officially launched, it is necessary to measure the impact of road environment on passengers. Among the latent variables, they have found that discovering new travel ways interest and hedonic value positively impact on the choice utility. It means that the bus services provide more fun and interest in the process, it would increase the choice utility. The result also showed that the ICLV model with latent variable was better than basic MNL model. It means that when evaluation the choice of autonomous bus service, not only considering its service attributes and social demographic variables, but also considering the individual’s latent psychological variable. It would better to understand people actual choice behavior.

港都客運，「自駕巴士結合綠能運具、智慧體驗創新營運規劃計畫書」民110年。
經濟部，「自駕巴士彰濱鹿港觀光接駁運行計畫」民109年。
Ababio-Donkor, A., Saleh, W., & Fonzone, A. (2020). The role of personal norms in the choice of mode for commuting. Research in Transportation Economics, 83, 100966.
Abe, R. (2021). Preferences of urban rail users for first- and last-mile autonomous vehicles: Price and service elasticities of demand in a multimodal environment. Transportation Research Part C: Emerging Technologies, 126, 103105.
Abe, R., Kita, Y., & Fukuda, D. (2020). An Experimental Approach to Understanding the Impacts of Monitoring Methods on Use Intentions for Autonomous Vehicle Services: Survey Evidence from Japan. Sustainability, 12(6), 2157.
Battistini, R., Mantecchini, L., & Postorino, M. N. (2020). Users’ Acceptance of Connected and Automated Shuttles for Tourism Purposes: A Survey Study. Sustainability, 12(23), 10188.
Ben-Akiva, M., McFadden, D., Train, K., Walker, J., Bhat, C., Bierlaire, M., . . . Bunch, D. S. (2002). Hybrid choice models: Progress and challenges. Marketing Letters, 13(3), 163-175.
Ben-Akiva, M., Walker, J., Bernardino, A. T., Gopinath, D. A., Morikawa, T., & Polydoropoulou, A. (2002). Integration of choice and latent variable models. Perpetual motion: Travel behaviour research opportunities and application challenges, 431-470.
Bennett, R., Vijaygopal, R., & Kottasz, R. (2019). Attitudes towards autonomous vehicles among people with physical disabilities. Transportation Research Part A: Policy and Practice, 127, 1-17.
. Chapter 6 - CityMobil2 Impacts Seen from Outside. (2018). In A. Alessandrini (Ed.), Implementing Automated Road Transport Systems in Urban Settings (pp. 295-310): Elsevier.
Chee, P. N. E., Susilo, Y. O., Wong, Y. D., & Pernestål, A. (2020). Which factors affect willingness-to-pay for automated vehicle services? Evidence from public road deployment in Stockholm, Sweden. European Transport Research Review, 12(1), 20.
Chen, C.-F. (2019). Factors affecting the decision to use autonomous shuttle services: Evidence from a scooter-dominant urban context. Transportation Research Part F: Traffic Psychology and Behaviour, 67, 195-204.
Cohen, S. A., & Hopkins, D. (2019). Autonomous vehicles and the future of urban tourism. Annals of Tourism Research, 74, 33-42.
Egbue, O., & Long, S. (2012). Barriers to widespread adoption of electric vehicles: An analysis of consumer attitudes and perceptions. Energy Policy, 48, 717-729.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
Gavalas, D., Konstantopoulos, C., Mastakas, K., & Pantziou, G. (2014). Mobile recommender systems in tourism. Journal of Network and Computer Applications, 39, 319-333.
Guo, J., Susilo, Y., Antoniou, C., & Pernestål, A. (2021). When and why do people choose automated buses over conventional buses? Results of a context-dependent stated choice experiment. Sustainable Cities and Society, 69, 102842.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis: Pearson new international edition. Essex: Pearson Education Limited, 1(2).
Hilgarter, K., & Granig, P. (2020). Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation Research Part F: Traffic Psychology and Behaviour, 72, 226-243.
Hirschman, E. C., & Holbrook, M. B. (1982). Hedonic consumption: Emerging concepts, methods and propositions. Journal of Marketing, 46(3), 92-101.
Kapser, S., & Abdelrahman, M. (2020). Acceptance of autonomous delivery vehicles for last-mile delivery in Germany – Extending UTAUT2 with risk perceptions. Transportation Research Part C: Emerging Technologies, 111, 210-225.
Kim, E.-J., Kim, Y., Jang, S., & Kim, D.-K. (2021). Tourists’ preference on the combination of travel modes under Mobility-as-a-Service environment. Transportation Research Part A: Policy and Practice, 150, 236-255.
Kim, J., Rasouli, S., & Timmermans, H. J. P. (2017). The effects of activity-travel context and individual attitudes on car-sharing decisions under travel time uncertainty: A hybrid choice modeling approach. Transportation Research Part D: Transport and Environment, 56, 189-202.
Kim, Y., Kim, E.-J., Jang, S., & Kim, D.-K. (2021). A comparative analysis of the users of private cars and public transportation for intermodal options under Mobility-as-a-Service in Seoul. Travel Behaviour and Society, 24, 68-80.
Krueger, R., Rashidi, T. H., & Rose, J. M. (2016). Preferences for shared autonomous vehicles. Transportation Research Part C: Emerging Technologies, 69, 343-355.
Madigan, R., Louw, T., Wilbrink, M., Schieben, A., & Merat, N. (2017). What influences the decision to use automated public transport? Using UTAUT to understand public acceptance of automated road transport systems. Transportation Research Part F: Traffic Psychology and Behaviour, 50, 55-64.
Nastjuk, I., Herrenkind, B., Marrone, M., Brendel, A. B., & Kolbe, L. M. (2020). What drives the acceptance of autonomous driving? An investigation of acceptance factors from an end-user's perspective. Technological Forecasting and Social Change, 161, 120319.
Nazari, F., Noruzoliaee, M., & Mohammadian, A. (2018). Shared versus private mobility: Modeling public interest in autonomous vehicles accounting for latent attitudes. Transportation Research Part C: Emerging Technologies, 97, 456-477.
Nordhoff, S., Louw, T., Innamaa, S., Lehtonen, E., Beuster, A., Torrao, G., . . . Merat, N. (2020). Using the UTAUT2 model to explain public acceptance of conditionally automated (L3) cars: A questionnaire study among 9,118 car drivers from eight European countries. Transportation Research Part F: Traffic Psychology and Behaviour, 74, 280-297.
Ozaki, R., & Sevastyanova, K. (2011). Going hybrid: An analysis of consumer purchase motivations. Energy Policy, 39(5), 2217-2227.
Panagiotopoulos, I., & Dimitrakopoulos, G. (2018). An empirical investigation on consumers’ intentions towards autonomous driving. Transportation Research Part C: Emerging Technologies, 95, 773-784.
Pearmain, D., & Kroes, E. P. (1990). Stated preference techniques: a guide to practice.
Priporas, C.-V., Stylos, N., & Fotiadis, A. K. (2017). Generation Z consumers' expectations of interactions in smart retailing: A future agenda. Computers in Human Behavior, 77, 374-381.
Raveau, S., Álvarez-Daziano, R., Yáñez, M. F., Bolduc, D., & de Dios Ortúzar, J. (2010). Sequential and simultaneous estimation of hybrid discrete choice models: Some new findings. Transportation Research Record, 2156(1), 131-139.
Ribeiro, M. A., Gursoy, D., & Chi, O. H. (2021). Customer Acceptance of Autonomous Vehicles in Travel and Tourism. Journal of Travel Research, 0(0), 0047287521993578.
Sharma, I., & Mishra, S. (2020). Modeling consumers' likelihood to adopt autonomous vehicles based on their peer network. Transportation Research Part D: Transport and Environment, 87, 102509.
Thapa, D., Gabrhel, V., & Mishra, S. (2021). What are the factors determining user intentions to use AV while impaired? Transportation Research Part F: Traffic Psychology and Behaviour, 82, 238-255.
Van den Bergh, J., & Behrer, M. (2016). How cool brands stay hot: Branding to Generations Y and Z: Kogan Page Publishers.
Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 157-178.
Walker, J. L. (2001). Extended discrete choice models: integrated framework, flexible error structures, and latent variables. Massachusetts Institute of Technology,
Wood, S. (2013). Generation Z as consumers: trends and innovation. Institute for Emerging Issues: NC State University, 119(9), 7767-7779.
Xu, Z., Zhang, K., Min, H., Wang, Z., Zhao, X., & Liu, P. (2018). What drives people to accept automated vehicles? Findings from a field experiment. Transportation Research Part C: Emerging Technologies, 95, 320-334.
Yap, M. D., Correia, G., & van Arem, B. (2016). Preferences of travellers for using automated vehicles as last mile public transport of multimodal train trips. Transportation Research Part A: Policy and Practice, 94, 1-16.
Zhitomirsky-Geffet, M., & Blau, M. (2016). Cross-generational analysis of predictive factors of addictive behavior in smartphone usage. Computers in Human Behavior, 64, 682-693.
Zhou, F., Zheng, Z., Whitehead, J., Washington, S., Perrons, R. K., & Page, L. (2020). Preference heterogeneity in mode choice for car-sharing and shared automated vehicles. Transportation Research Part A: Policy and Practice, 132, 633-650.