Dynamic Modeling of Activity Happiness: An Investigation of the Intra-activity Hedonic Treadmill
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.
KeywordsHedonic treadmill Real-time happiness Retrospective happiness Duration neglect Smartphone data Future mobility sensing Dynamic ordinal logit model
- 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.
- Bierlaire, M., & Fetiarison, M. (2009). Estimation of discrete choice models: Extending BIOGEME. Proceedings of the 9th Swiss Transport Research Conference (STRC), Monte Verità.Google Scholar
- Duarte, A., Garcia, C., Limão, S., & Polydropoulou, A. (2008). Happiness in transport decision making: The Swiss sample. 8th Swiss Transport Research Conference, Ascona, Switzerland.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- Helliwell, J., Layard, R., & Sachs, J. (2017). World happiness report 2017. New York: Sustainable Development Solutions Network.Google Scholar
- Helson, H. (1964). Adaptation-level theory: An experimental and systematic approach to behavior. New York: Harper and Row.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- Kahneman, D., Diener, N., & Schwarz, N. (1999). Well-being: The foundations of hedonic psychology. New York: Russell Sage.Google Scholar
- March, J. G. (1988). Variable risk preferences and adaptive aspirations. Elsevier, 9(1), 5–24.Google Scholar
- Ory, D., & Mokhtarian, P. (2005). When is getting there half the fun? Modeling the liking for travel. Transportation Research Part A, 39, 97–123.Google Scholar
- Parducci, A. (1984). Value judgments: Toward a relational theory of happiness. Attitudinal judgement (pp. 3–21). New York: Springer-Verlag.Google Scholar
- 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.CrossRefGoogle Scholar
- Roddis, S. (2016). Victorian future mobility sensing (FMS) trial. Project Report. Department of Infrastructure Engineering, The University of Melbourne.Google Scholar
- 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.Google Scholar
- 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.Google Scholar