Abstract
While travel has traditionally been considered a means to reach activities, researchers have begun to investigate the effect it has on well-being. Improved surveying methods enabled by mobile phone applications, leveraging GPS, GSM, accelerometer, and WiFi, allow researchers to collect more complete data and test hypotheses related to individuals’ happiness with travel and activities. This chapter describes a data collection effort that took place in Melbourne, Australia using Future Mobility Sensing, a mobile phone application and web-based platform. Throughout the study, users were asked twice daily to report on happiness for a single activity, including travel. The chapter develops a dynamic Ordinal Logit Model based on the collected data and discusses the estimation results in the context of Hedonic Theory. The deviation of the reported happiness for an activity observation and an individual Set Point, defined as the median reported happiness of a user, is modeled as a function of covariates. The results show how different activity types (work, education, personal, discretionary, travel, staying at home, and other) affect individuals’ experienced happiness. It is found that educational activities, followed by work and travel, are the most disliked. Discretionary actives—which include social activities, meals, recreation, etc.—and other activities are seen to lead to more positive experiences of happiness. The model is used to test for the presence of an intra-activity Hedonic Treadmill Effect. It is found that people remember their activities as more neutral in later reports of happiness. The implications for the measurement of happiness data are discussed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abou-Zeid, M., & Ben-Akiva, M. (2012). Well-being and activity-based models. Transportation, 39, 1189–1207.
Andrews, F., & Withey, S. B. (1976). Developing measures of perceived life quality: Results from several national surveys. Social Indicators Research, 1, 1–26.
Ariely, D. (1998). Combining experiences over time: The effects of duration, intensity changes and on-line measurements on retrospective pain evaluations. Journal of Behavioral Decision Making, 11, 19–45.
Ariely, D., & Zauberman, G. (2000). On the making of an experience: The effects of breaking and combining experiences on their overall evaluation. Journal of Behavioral Decision Making, 13, 219–232.
Australian Bureau of Statistics. (2011). Highest level of education (all persons aged 15 years and over). http://www.abs.gov.au/websitedbs/censushome.nsf/home/mediafactsheets2nd/$file/Topic%20-%20Highest%20Level%20of%20Education.pdf. Accessed 23 June 2017.
Australian Bureau of Statistics. (2017). Household income and wealth, Australia 2015–16. http://www.abs.gov.au/ausstats/abs@.nsf/mf/6523.0. Accessed 23 June 2017.
Baumeister, R. F., Vohs, K. D., & Oettingen, G. (2016). Pragmatic prospection: How and why people think about the future. Review of General Psychology, 20(1), 3–16.
Bergstad, C. J., Gamble, A., Hagman, O., Polk, M., Gärling, T., Ettema, D., Friman, M., & Olson, L. E. (2012). Influences of affect associated with routine out-of-house activities on subjective well-being. Applied Research in Quality of Life, 7, 49–62.
Bierlaire, M., & Fetiarison, M. (2009). Estimation of discrete choice models: Extending BIOGEME. Proceedings of the 9th Swiss Transport Research Conference (STRC), Monte Verità.
Cottrill, C., Pereira, F., Zhao, F., Dias, I., Lim, H., Ben-Akiva, M., & Zegras, P. C. (2013). Future mobility survey. Transportation Research Record: Journal of the Transportation Research Board, 2354, 59–67.
Diener, E. (1984). Subjective well-being. Psychological Bulletin, 95(3), 542–575.
Duarte, A., Garcia, C., Limão, S., & Polydropoulou, A. (2008). Happiness in transport decision making: The Swiss sample. 8th Swiss Transport Research Conference, Ascona, Switzerland.
Ettema, D., Gärling, T., Eriksson, L., Friman, M., Olsson, L. E., & Fujii, S. (2011). Satisfaction with travel and subjective well-being: Development and test of a measurement tool. Transportation Research Part F, 14, 167–175.
Frederick, S., & Loewenstein, G. (1999). Hedonic adaptation. In D. Kahneman, E. Diener, & N. Schwarz (Eds.), Well-being: The foundations of hedonic psychology (pp. 302–329). New York: Russell Sage.
Fujita, F., & Diener, E. (2005). Life satisfaction set point: Stability and change. Journal of Personality and Social Psychology, 88(1), 158–164.
Ghorpade, A., Pereira, F. C., Zhao, F., Zegras, C., Ben-Akiva, M. (2015). An integrated stop-mode detection algorithm for real world smartphone-based travel survey. 94th Annual Meeting of the Transportation Research Board, Washington, DC.
Giachanou, A., & Crestani, F. (2016) Like it or not: A survey of Twitter sentiment analysis methods. ACM Computing Surveys, 49(2), 28–28:41.
Hardie, B. G. S., Johnson, E. J., & Fader, P. S. (1993). Modeling loss aversion and brand dependence effects on brand choice. Marketing Science, 12, 378–394.
Helliwell, J., Layard, R., & Sachs, J. (2017). World happiness report 2017. New York: Sustainable Development Solutions Network.
Helson, H. (1964). Adaptation-level theory: An experimental and systematic approach to behavior. New York: Harper and Row.
Jariyasunan, J., Carrel, A., Ekambaram, V., Gaker, D. J., Kote, T., Sengupta, R., Walker, J. L. (2012). The quantified traveler: Using personal travel data to promote sustainable transport behavior. 91st Annual Meeting of the Transportation Research Board, Washington, DC.
Kahneman, D. (1999). Evaluation by moments: Past and future. In D. Kahneman & A. Tversky (Eds.), Choices, values and frames. New York: Cambridge University Press and Russell Sage.
Kahneman, D., & Krueger, A. B. (2006). Developments in the measurement of subjective well-being. Journal of Economic Perspectives, 20(1), 3–24.
Kahneman, D., Diener, N., & Schwarz, N. (1999). Well-being: The foundations of hedonic psychology. New York: Russell Sage.
Kahneman, D., Fredrickson, B., Schreiber, C., & Redelmeier, D. (1993). When more pain is preferred to less: Adding a better end. Psychological Science, 4(6), 401–405. Retrieved from http://www.jstor.org/stable/40062570
Kahneman, D., Krueger, A. B., Schkade, D. A., Schwarz, N., & Stone, A. A. (2004). A survey method for characterizing daily life experience: The day reconstruction method. Science, 306(5702), 1776–1780.
Killingsworth, M., & Gilbert, D. (2010). A wandering mind is an unhappy mind. Science, 330(6006), 932.
March, J. G. (1988). Variable risk preferences and adaptive aspirations. Elsevier, 9(1), 5–24.
Ory, D., & Mokhtarian, P. (2005). When is getting there half the fun? Modeling the liking for travel. Transportation Research Part A, 39, 97–123.
Oswald, A. J. (1997). Happiness and economic performance. Economic Journal, 107(445), 1815–1831.
Parducci, A. (1968). The Relativism of absolute judgments. Scientific American, 219, 84–90.
Parducci, A. (1984). Value judgments: Toward a relational theory of happiness. Attitudinal judgement (pp. 3–21). New York: Springer-Verlag.
Pedersen, T., Friman, M., & Kristensson, P. (2011). The role of predicted, on-line experienced and remembered satisfaction in current choice to use public transport services. Journal of Retailing and Consumer Services, 18, 471–475.
Raveau, S., Ghorpade, A., Zhao, F., Abou-Zeid, M., Zegras, C., & Ben-Akiva, M. (2015). Smartphone-based survey for real-time and retrospective happiness related to travel and activities. Transportation Research Record: Journal of the Transportation Research Board, 2566, 102–110.
Ravulaparthy, S., Yoon, S., & Goulias, K. (2013). Linking elderly transport mobility and subjective well-being: A multivariate latent modeling approach. Transportation Research Record: Journal of the Transportation Research Board, 2382, 28–36.
Roddis, S. (2016). Victorian future mobility sensing (FMS) trial. Project Report. Department of Infrastructure Engineering, The University of Melbourne.
Ryder, H. R., & Heal, G. M. (1973). Optimal growth with intertemporally dependent preferences. Review of Economic Studies, 40, 1–33.
Sinnott, R. O., & Cui, S. (2016). Benchmarking sentiment analysis approaches on the cloud. In IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS) (pp. 695–704). Wuhan: IEEE.
The Economist Intelligence Unit. (2017). The global liveability report 2017. The Economist Intelligence Unit Limited 2017. http://www.eiu.com/Handlers/WhitepaperHandler.ashx?fi=Liveability-Ranking-Free-Summary-Report-August-2017.pdf
The Victorian Department of Transport. (2011). Victorian integrated survey of travel & activity 2009–10. Survey Procedures and Documentation. Final Data Release v1.0.
Wirtz, D., Kruger, J., Scollon, C. N., & Diener, E. (2003). Research report: What to do on spring break? The role of predicted, on-line, and remembered experience in future choice. Psychological Science, 14(5), 520–524.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
de Lima, I.V. et al. (2018). Dynamic Modeling of Activity Happiness: An Investigation of the Intra-activity Hedonic Treadmill. In: Friman, M., Ettema, D., Olsson, L.E. (eds) Quality of Life and Daily Travel. Applying Quality of Life Research. Springer, Cham. https://doi.org/10.1007/978-3-319-76623-2_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-76623-2_6
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-76622-5
Online ISBN: 978-3-319-76623-2
eBook Packages: Social SciencesSocial Sciences (R0)