IDEAS home Printed from https://ideas.repec.org/p/cdl/uctcwp/qt2dh952gj.html
   My bibliography  Save this paper

Quantified Traveler: Travel Feedback Meets the Cloud to Change Behavior

Author

Listed:
  • Jariyasunant, Jerald
  • Abou-Zeid, Maya
  • Carrel, Andre
  • Ekambaram, Venkatesan
  • Gaker, David
  • Sengupta, Raja
  • Walker, Joan L.

Abstract

We describe the design and evaluation of a system named Quantified Traveler (QT). QT is a Computational Travel Feedback System. Travel Feedback is an established programmatic method whereby travelers record travel in diaries, and meet with a counselor who guides her to alternate mode or trip decisions that are more sustainable or otherwise beneficial to society, while still meeting the subject’s mobility needs. QT is a computation surrogate for the counselor. Since counselor costs can limit the size of travel feedback programs, a system such as QT at the low costs of cloud computing, could dramatically increase scale, and thereby sustainable travel. QT uses an app on the phone to collect travel data, a server in the cloud to process it into travel diaries and then a personalized carbon, exercise, time, and cost footprint. The subject is able to see all of this information on the web. We evaluate with 135 subjects to learn if subjects let us use their personal phones and data-plans to build travel diaries, whether they actually use the website to look at their travel information, whether the design creates pro-environmental shifts in psychological variables measured by entry and exit surveys, and finally whether the revealed travel behavior records reduced driving. Before and after statistical analysis and the results from a structural equation model suggest that the results are a qualified success.

Suggested Citation

  • Jariyasunant, Jerald & Abou-Zeid, Maya & Carrel, Andre & Ekambaram, Venkatesan & Gaker, David & Sengupta, Raja & Walker, Joan L., 2013. "Quantified Traveler: Travel Feedback Meets the Cloud to Change Behavior," University of California Transportation Center, Working Papers qt2dh952gj, University of California Transportation Center.
    • Handle: RePEc:cdl:uctcwp:qt2dh952gj
    as

    Download full text from publisher

    File URL: https://www.escholarship.org/uc/item/2dh952gj.pdf;origin=repeccitec
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chester, Mikhail V, 2008. "Life-cycle Environmental Inventory of Passenger Transportation in the United States," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt7n29n303, Institute of Transportation Studies, UC Berkeley.
    2. Chen, Ching-Fu & Lai, Wen-Tai, 2011. "The effects of rational and habitual factors on mode choice behaviors in a motorcycle-dependent region: Evidence from Taiwan," Transport Policy, Elsevier, vol. 18(5), pages 711-718, September.
    3. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    4. Choo, Sangho & Mokhtarian, Patricia L., 2004. "What type of vehicle do people drive? The role of attitude and lifestyle in influencing vehicle type choice," University of California Transportation Center, Working Papers qt7vg1057g, University of California Transportation Center.
    5. Tim Schwanen & Patricia L. Mokhtarian, 2007. "Attitudes toward travel and land use and choice of residential neighborhood type: Evidence from the San Francisco bay area," Housing Policy Debate, Taylor & Francis Journals, vol. 18(1), pages 171-207, January.
    6. Choo, Sangho & Mokhtarian, Patricia L., 2004. "What type of vehicle do people drive? The role of attitude and lifestyle in influencing vehicle type choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 38(3), pages 201-222, March.
    7. Vij, Akshay & Carrel, André & Walker, Joan L., 2013. "Incorporating the influence of latent modal preferences on travel mode choice behavior," Transportation Research Part A: Policy and Practice, Elsevier, vol. 54(C), pages 164-178.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yuntao Guo & Srinivas Peeta & Shubham Agrawal & Irina Benedyk, 2022. "Impacts of Pokémon GO on route and mode choice decisions: exploring the potential for integrating augmented reality, gamification, and social components in mobile apps to influence travel decisions," Transportation, Springer, vol. 49(2), pages 395-444, April.
    2. Stathopoulos, Amanda & Cirillo, Cinzia & Cherchi, Elisabetta & Ben-Elia, Eran & Li, Yeun-Touh & Schmöcker, Jan-Dirk, 2017. "Innovation adoption modeling in transportation: New models and data," Journal of choice modelling, Elsevier, vol. 25(C), pages 61-68.
    3. Evangelia Anagnostopoulou & Efthimios Bothos & Babis Magoutas & Johann Schrammel & Gregoris Mentzas, 2018. "Persuasive Technologies for Sustainable Mobility: State of the Art and Emerging Trends," Sustainability, MDPI, vol. 10(7), pages 1-22, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jariyasunant, Jerald & Carrel, Andre & Ekambaram, Venkatesan & Gaker, David & Sengupta, Raja & Walker, Joan L., 2012. "The Quantified Traveler: Changing transport behavior with personalized travel data feedback," University of California Transportation Center, Working Papers qt3047k0dw, University of California Transportation Center.
    2. Nayum, Alim & Klöckner, Christian A. & Prugsamatz, Sunita, 2013. "Influences of car type class and carbon dioxide emission levels on purchases of new cars: A retrospective analysis of car purchases in Norway," Transportation Research Part A: Policy and Practice, Elsevier, vol. 48(C), pages 96-108.
    3. Wang, Tingting & Chen, Cynthia, 2012. "Attitudes, mode switching behavior, and the built environment: A longitudinal study in the Puget Sound Region," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(10), pages 1594-1607.
    4. Silm, Siiri & Tominga, Ago & Saidla, Karl & Poom, Age & Tammaru, Tiit, 2024. "Socio-economic and residential differences in urban modality styles based on a long-term smartphone experiment," Journal of Transport Geography, Elsevier, vol. 115(C).
    5. Ory, David T, 2007. "Structural Equation Modeling of Relative Desired Travel Amounts," Institute of Transportation Studies, Working Paper Series qt8mj659fp, Institute of Transportation Studies, UC Davis.
    6. Van Acker, Veronique & Mokhtarian, Patricia L. & Witlox, Frank, 2014. "Car availability explained by the structural relationships between lifestyles, residential location, and underlying residential and travel attitudes," Transport Policy, Elsevier, vol. 35(C), pages 88-99.
    7. Ali Ardeshiri & Akshay Vij, 2019. "A lifestyle-based model of household neighbourhood location and individual travel mode choice behaviours," Papers 1902.01986, arXiv.org, revised Nov 2019.
    8. Maya Abou-Zeid & Moshe Ben-Akiva, 2014. "Hybrid choice models," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 17, pages 383-412, Edward Elgar Publishing.
    9. Carlo Giacomo Prato & Katrín Halldórsdóttir & Otto Anker Nielsen, 2017. "Latent lifestyle and mode choice decisions when travelling short distances," Transportation, Springer, vol. 44(6), pages 1343-1363, November.
    10. Vij, Akshay & Gorripaty, Sreeta & Walker, Joan L., 2017. "From trend spotting to trend ’splaining: Understanding modal preference shifts in the San Francisco Bay Area," Transportation Research Part A: Policy and Practice, Elsevier, vol. 95(C), pages 238-258.
    11. Qu, Ying & Liu, Yakun & Zhu, Qinghua & Liu, Yue, 2014. "Motivating small-displacement car purchasing in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 67(C), pages 47-58.
    12. Ory, David Terrance, 2007. "Structural Equation Modeling of Relative Desired Travel Amounts," University of California Transportation Center, Working Papers qt7rb3x52m, University of California Transportation Center.
    13. Vij, Akshay & Walker, Joan L., 2014. "Preference endogeneity in discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 64(C), pages 90-105.
    14. Matthew Wigginton Bhagat-Conway & Laura Mirtich & Deborah Salon & Nathan Harness & Alexis Consalvo & Shuyao Hong, 2024. "Subjective variables in travel behavior models: a critical review and Standardized Transport Attitude Measurement Protocol (STAMP)," Transportation, Springer, vol. 51(1), pages 155-191, February.
    15. Alemi, Farzad & Circella, Giovanni & Mokhtarian, Patricia & Handy, Susan, 2018. "Exploring the latent constructs behind the use of ridehailing in California," Journal of choice modelling, Elsevier, vol. 29(C), pages 47-62.
    16. Axsen, Jonn & TyreeHageman, Jennifer & Lentz, Andy, 2012. "Lifestyle practices and pro-environmental technology," Ecological Economics, Elsevier, vol. 82(C), pages 64-74.
    17. Jahun Koo & Sangho Choo, 2022. "Identification of Causal Relationship between Attitudinal Factors and Intention to Use Transportation Mode," Sustainability, MDPI, vol. 14(24), pages 1-15, December.
    18. Vredin Johansson, Maria & Heldt, Tobias & Johansson, Per, 2006. "The effects of attitudes and personality traits on mode choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(6), pages 507-525, July.
    19. Cristiano Codagnone & Giuseppe Alessandro Veltri & Francesco Bogliacino & Francisco Lupiáñez-Villanueva & George Gaskell & Andriy Ivchenko & Pietro Ortoleva & Francesco Mureddu, 2016. "Labels as nudges? An experimental study of car eco-labels," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 33(3), pages 403-432, December.
    20. Wårell, Linda & Ek, Kristina, 2024. "Male lone wolves and sociable females – Preferences for shared and AV transport services," Research in Transportation Economics, Elsevier, vol. 108(C).

    More about this item

    Keywords

    ;
    ;
    ;

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cdl:uctcwp:qt2dh952gj. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Lisa Schiff (email available below). General contact details of provider: https://edirc.repec.org/data/itucbus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.