Transportation

, Volume 45, Issue 2, pp 429–449 | Cite as

Activity involvement and time spent on computers for leisure: an econometric analysis on the American Time Use Survey dataset

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Abstract

Internet is capturing more and more of our time each day, and the increasing levels of engagement are mainly due to the use of social media. Time spent on social media is observed in the American Time Use Survey and recorded as leisure time on Personal Computer (PC). In this paper, we extend the traditional analysis of leisure activity participation by including leisure activities that require the use of a PC. We study the substitution effects with both in-home and out-of-home leisure activities and the time budget allocated to each of them. The modeling framework that includes both discrete alternatives and continuous decision variables allow for full correlation across the utility of the alternatives that are all of leisure type and the regressions that model the time allocated to each activity. Results show that there is little substitution effect between leisure with PC and the relative time spent on it, with in-home and out-of-home leisure episodes. Households with more children and full-time workers are more likely to engage in in-home and PC related leisure activities (especially during weekends). Increments in the travel time of social trips result in significant reductions in leisure time during weekdays.

Keywords

Discrete–continuous choice model Social media Leisure activity Activity-travel pattern Time use 

References

  1. Aguiléra, A., Guillot, C., Rallet, A.: Mobile ICTs and physical mobility: review and research agenda. Transp. Res. Part A Policy Pract. 46(4), 664–672 (2012)CrossRefGoogle Scholar
  2. Akar, G., Clifton, K.J., Doherty, S.T.: Discretionary activity location choice: in-home or out-of-home? Transportation 38(1), 101–122 (2011)CrossRefGoogle Scholar
  3. Akar, G., Clifton, K.J., Doherty, S.T.: Redefining activity types: Who participates in which leisure activity? Transp. Res. Part A Policy Pract. 46(8), 1194–1204 (2012)CrossRefGoogle Scholar
  4. Ben-Elia, E., Alexander, B., Hubers, C., Ettema, D.: Activity fragmentation, ICT and travel: an exploratory path analysis of spatiotemporal interrelationships. Transp. Res. Part A Policy Pract. 68, 56–74 (2014)CrossRefGoogle Scholar
  5. Balepur, P.N., Varma, K.V., Mokhtarian, P.L.: Transportation impacts of center-based telecommuting: interim findings from the neighborhood telecenters project. Transportation 25(3), 287–306 (1998)CrossRefGoogle Scholar
  6. Bhat, C.R.: A multiple discrete–continuous extreme value model: formulation and application to discretionary time-use decisions. Transp. Res. Part B Methodol. 39(8), 679–707 (2005)CrossRefGoogle Scholar
  7. Bhat, C.R., Gossen, R.: A mixed multinomial logit model analysis of weekend recreational episode type choice. Transp. Res. Part B Methodol. 38(9), 767–787 (2004)CrossRefGoogle Scholar
  8. Bhat, C.R., Koppelman, F.S.: A retrospective and prospective survey of time-use research. Transportation 26(2), 119–139 (1999)CrossRefGoogle Scholar
  9. Bhat, C., Lockwood, A.: On distinguishing between physically active and physically passive episodes and between travel and activity episodes: an analysis of weekend recreational participation in the San Francisco Bay area. Transp. Res. Part A Policy Pract. 38(8), 573–592 (2004)CrossRefGoogle Scholar
  10. Bhat, C.R., Misra, R.: Discretionary activity time allocation of individuals between in-home and out-of-home and between weekdays and weekends. Transportation 26(2), 193–209 (1999)CrossRefGoogle Scholar
  11. BLS: American Time Use Survey—2013 Results. https://www.bls.gov/news.release/archives/atus_06182014.pdf (2013)
  12. Bureau of Labor Statistics: 2013 American Time Use Survey [Data file and code book.]. https://www.bls.gov/tus/datafiles_2013.htm (2014)
  13. Cao, X., Douma, F., Cleaveland, F.: Influence of E-shopping on shopping travel: evidence from Minnesota’s Twin Cities. Transp. Res. Rec. J. Transp. Res. Board 2157, 147–154 (2010)CrossRefGoogle Scholar
  14. Carrasco, J.A., Miller, E.J.: The social dimension in action: a multilevel, personal networks model of social activity frequency between individuals. Transp. Res. Part A Policy Pract. 43(1), 90–104 (2009)CrossRefGoogle Scholar
  15. Chaffey, D.: Mobile marketing statistics compilation. http://www.smartinsights.com/mobile-marketing/mobile-marketing-analytics/mobile-marketing-statistics/?new=1. Accessed Feb 2017 (2016)
  16. Chen, Y., Talebpour, A., Mahmassani, H.S.: Friends don’t let friends drive on bad routes: modeling the impact of social networks on drivers’ route choice behavior. In: Transportation Research Board 94th Annual Meeting (No. 15-4974) (2015)Google Scholar
  17. Copperman, R.B., Bhat, C.R.: An analysis of the determinants of children’s weekend physical activity participation. Transportation 34(1), 67–87 (2007)CrossRefGoogle Scholar
  18. Dal Fiore, F., Mokhtarian, P.L., Salomon, I., Singer, M.E.: “Nomads at last”? A set of perspectives on how mobile technology may affect travel. J. Transp. Geogr. 41, 97–106 (2014)CrossRefGoogle Scholar
  19. D’Andrea, E., Ducange, P., Lazzerini, B., Marcelloni, F.: Real-time detection of traffic from twitter stream analysis. IEEE Trans. Intell. Transp. Syst. 16(4), 2269–2283 (2015)CrossRefGoogle Scholar
  20. Dey, S.S., Thommana, J., Dock, S.: Public agency performance management for improved service delivery in the digital age: case study. J. Manag. Eng. 31(5), 05014022 (2014)CrossRefGoogle Scholar
  21. Diana, M., Cirillo, C., van Arem, B.: Relationship between computer use for leisure and travel patterns: a study on the American time use survey (2005–2013). Submitted for publication (2016)Google Scholar
  22. Douma, F., Wells, K., Horan, T.A., Krizek, K.J.: ICT and travel in the twin cities metropolitan area: enacted patterns between internet use and working and shopping trips. In: Proceedings CD-ROM of the 83rd Annual Meeting of the Transportation Research Board, Washington DC (2004)Google Scholar
  23. Farag, S., Weltevreden, J., Van Rietbergen, T., Dijst, M., van Oort, F.: E-shopping in the Netherlands: Does geography matter? Environ. Plan. 33(1), 59–74 (2006)CrossRefGoogle Scholar
  24. Farag, S., Schwanen, T., Dijst, M., Faber, J.: Shopping online and/or in-store? A structural equation model of the relationships between e-shopping and in-store shopping. Transp. Res. Part A Policy Pract. 41(2), 125–141 (2007)CrossRefGoogle Scholar
  25. Ferrell, C.: Home-based teleshopping and shopping travel: Where do people find the time? Transp. Res. Rec. J. Transp. Res. Board 1926, 212–223 (2005)CrossRefGoogle Scholar
  26. Gal-Tzur, A., Grant-Muller, S.M., Kuflik, T., Minkov, E., Nocera, S., Shoor, I.: The potential of social media in delivering transport policy goals. Transp. Policy 32, 115–123 (2014)CrossRefGoogle Scholar
  27. Garikapati, V.M., Pendyala, R.M., Morris, E.A., Mokhtarian, P.L., McDonald, N.: Activity patterns, time use, and travel of millennials: a generation in transition? Transp. Rev. 36, 558–584 (2016). doi: 10.1080/01441647.2016.1197337 CrossRefGoogle Scholar
  28. Gkiotsalitis, K., Alesiani, F., Baldessari, R.: Educated rules for the prediction of human mobility patterns based on sparse social media and mobile phone data. In: Transportation Research Board 93rd Annual Meeting (No. 14-0745) (2014)Google Scholar
  29. Goldfarb, A., Greenstein, S.M., Tucker, C.E.: Economic analysis of the digital economy. University of Chicago Press, Chicago (2015)CrossRefGoogle Scholar
  30. Gu, Y., Qian, Z.S., Chen, F.: From Twitter to detector: real-time traffic incident detection using social media data. Transp. Res. Part C Emerg. Technol. 67, 321–342 (2016)CrossRefGoogle Scholar
  31. Habib, K., Carrasco, J., Miller, E.: Social context of activity scheduling: discrete-continuous model of relationship between” with whom” and episode start time and duration. Transp. Res. Rec. J. Transp. Res. Board 2076, 81–87 (2008)CrossRefGoogle Scholar
  32. Habib, K.M.N., Day, N., Miller, E.J.: An investigation of commuting trip timing and mode choice in the Greater Toronto Area: application of a joint discrete-continuous model. Transp. Res. Part A Policy Pract. 43(7), 639–653 (2009)CrossRefGoogle Scholar
  33. Hamer, R., Kroes, E., Van Ooststroom, H.: Teleworking in the Netherlands: an evaluation of changes in travel behaviour. Transportation 18(4), 365–382 (1991)CrossRefGoogle Scholar
  34. Handy, S.L., Mokhtarian, P.L.: The future of telecommuting. Futures 28(3), 227–240 (1996)CrossRefGoogle Scholar
  35. Hasan, S., Ukkusuri, S.V.: Urban activity pattern classification using topic models from online geo-location data. Transp. Res. Part C Emerg. Technol. 44, 363–381 (2014)CrossRefGoogle Scholar
  36. Kapur, A., Bhat, C.: Modeling adults’ weekend day-time use by activity purpose and accompaniment arrangement. Transp. Res. Rec. J. Transp. Res. Board 2021, 18–27 (2007)CrossRefGoogle Scholar
  37. Kemperman, A.D., Timmermans, H.J.: Influence of socio-demographics and residential environment on leisure activity participation. Leisure Sci. 30(4), 306–324 (2008)CrossRefGoogle Scholar
  38. Kitamura, R., Yamamoto, T., Susilo, Y.O., Axhausen, K.W.: How routine is a routine? An analysis of the day-to-day variability in prism vertex location. Transp. Res. Part A Policy Pract. 40(3), 259–279 (2006)CrossRefGoogle Scholar
  39. Le Vine, S., Latinopoulos, C., Polak, J.: Analysis of the relationship between internet usage and allocation of time for personal travel and out of home activities: case study of Scotland in 2005/6. Travel Behav. Soc. 4, 49–59 (2016)CrossRefGoogle Scholar
  40. Lee, J.H., Gao, S., Goulias, K.G.: Comparing the origin-destination matrices from travel demand model and social media data. In: Transportation Research Board 95th Annual Meeting (No. 16-0069) (2016)Google Scholar
  41. Lin, T., Wang, D.: Tradeoffs between in-and out-of-residential neighborhood locations for discretionary activities and time use: do social contexts matter? J. Transp. Geogr. 47, 119–127 (2015)CrossRefGoogle Scholar
  42. Liu, Y., Cirillo, C.: Model system to evaluate impacts of vehicle purchase tax and fuel tax on household greenhouse gas emissions. Transp. Res. Rec. J. Transp. Res. Board 2503, 51–59 (2015)CrossRefGoogle Scholar
  43. Liu, Y., Tremblay, J.M., Cirillo, C.: An integrated model for discrete and continuous decisions with application to vehicle ownership, type and usage choices. Transp. Res. Part A Policy Pract. 69, 315–328 (2014)CrossRefGoogle Scholar
  44. Maghrebi, M., Abbasi, A., Rashidi, T.H., Waller, S.T.: Complementing travel diary surveys with twitter data: application of text mining techniques on activity location, type and time. In: 2015 IEEE 18th International Conference on Intelligent Transportation Systems (pp. 208–213) (2015)Google Scholar
  45. McCulloch, C.E., Neuhaus, J.M.: Generalized linear mixed models. Wiley, New York (2001)Google Scholar
  46. McNally, M.G., Rindt, C.: The activity-based approach. Technical Report n. UCI-ITS-WP-07-1. D. Institute of Transportation Studies University of California, Irvine, USA (2007)Google Scholar
  47. Meloni, I., Spissu, E., Bez, M.: A model of the dynamic process of time allocation to discretionary activities. Transp. Sci. 41(1), 15–28 (2007)CrossRefGoogle Scholar
  48. Mokhtarian, P. L. (1991). Defining telecommuting. Research Report n. UCD-ITS-RR-91-04. Institute of Transportation Studies University of California, Davis, USAGoogle Scholar
  49. Mokhtarian, P.L., Handy, S.L., Salomon, I.: Methodological issues in the estimation of the travel, energy, and air quality impacts of telecommuting. Transp. Res. Part A Policy Pract. 29(4), 283–302 (1995)CrossRefGoogle Scholar
  50. Mokhtarian, P.L., Salomon, I.: Modeling the desire to telecommute: the importance of attitudinal factors in behavioral models. Transp. Res. Part A Policy Pract. 31(1), 35–50 (1997)CrossRefGoogle Scholar
  51. Mokhtarian, P.L.: The transportation impacts of telecommuting: recent empirical findings. In: Stopher, P., Lee Gosselin, M. (eds.) Understanding Travel Behaviour in an Era of Change, pp. 91–106. Oxford, Elsevier (1997)Google Scholar
  52. Mokhtarian, P.L.: A synthetic approach to estimating the impacts of telecommuting on travel. Urban Stud. 35(2), 215–241 (1998)CrossRefGoogle Scholar
  53. Mokhtarian, P.L.: A conceptual analysis of the transportation impacts of B2C e-commerce. Transportation 31(3), 257–284 (2004)CrossRefGoogle Scholar
  54. Mokhtarian, P.L., Salomon, I., Handy, S.L.: The impacts of ICT on leisure activities and travel: a conceptual exploration. Transportation 33(3), 263–289 (2006)CrossRefGoogle Scholar
  55. Mondschein, A.: Five-star transportation: using online activity reviews to examine mode choice to non-work destinations. Transportation 42(4), 707–722 (2015)CrossRefGoogle Scholar
  56. Paleti, R., Copperman, R.B., Bhat, C.R.: An empirical analysis of children’s after school out-of-home activity-location engagement patterns and time allocation. Transportation 38(2), 273–303 (2011)CrossRefGoogle Scholar
  57. Pender, B., Currie, G., Delbosc, A., Shiwakoti, N.: Social media use during unplanned transit network disruptions: a review of literature. Transp. Rev. 34(4), 501–521 (2014)CrossRefGoogle Scholar
  58. Pendyala, R.M., Goulias, K.G., Kitamura, R.: Impact of telecommuting on spatial and temporal patterns of household travel. Transportation 18(4), 383–409 (1991)CrossRefGoogle Scholar
  59. Pendyala, R.M., Yamamoto, T., Kitamura, R.: On the formulation of time-space prisms to model constraints on personal activity-travel engagement. Transportation 29(1), 73–94 (2002)CrossRefGoogle Scholar
  60. Pinjari, A.R., Bhat, C.R.: A multiple discrete-continuous nested extreme value (MDCNEV) model: formulation and application to non-worker activity time-use and timing behavior on weekdays. Transp. Res. Part B 44(4), 562–583 (2010)CrossRefGoogle Scholar
  61. Robinson, J.P., Martin, S.: IT use and declining social capital? More cold water from the General Social Survey (GSS) and the American Time-Use Survey (ATUS). Soc. Sci. Comput. Rev. 28(1), 45–63 (2010)CrossRefGoogle Scholar
  62. Schwanen, T., Kwan, M.P.: The internet, mobile phone and space–time constraints. Geoforum 39(3), 1362–1377 (2008)CrossRefGoogle Scholar
  63. Scanzoni, J.H., Szinovacz, M.E.: Family Decision-Making: A Developmental Sex Role Model. Beverly Hills, Sage (1980)Google Scholar
  64. Srinivasan, S., Bhat, C.R.: An exploratory analysis of joint-activity participation characteristics using the American time use survey. Transportation 35(3), 301–327 (2008)CrossRefGoogle Scholar
  65. Train, K.: Discrete Choice Methods with Simulation. Cambridge University Press (2009)CrossRefGoogle Scholar
  66. Veenhof, B.: The Internet: Is it Changing the way Canadians Spend Their Time?. Statistics Canada, Ottawa (2006)Google Scholar
  67. Visser, E.J., Lanzendorf, M.: Mobility and accessibility effects of b2c e-commerce: a literature review. Tijdschr. Econ. Soc. Geogr. 95, 189–205 (2004)CrossRefGoogle Scholar
  68. Wang, D., Law, F.Y.T.: Impacts of information and communication technologies (ICT) on time use and travel behavior: a structural equations analysis. Transportation 34(4), 513–527 (2007)CrossRefGoogle Scholar
  69. Weisberg, S.: Applied linear regression, vol. 528. Wiley, New York (2005)CrossRefGoogle Scholar
  70. Weltevreden, J.W.: Substitution or complementarity? How the internet changes city centre shopping. J. Retaili. Consum. Serv. 14(3), 192–207 (2007)CrossRefGoogle Scholar
  71. Wearesocial.: Digital in 2016. http://wearesocial.com/uk/special-reports/digital-in-2016 (2016). Accessed 26 Jan 2016 (2016)
  72. Wilson, R., Krizek, K., Handy, S.: Trends in out-of-home and at-home activities: Evidence from repeat cross-sectional surveys. Transp. Res. Rec. 2014, 76–84 (2007)CrossRefGoogle Scholar
  73. Yamamoto, T., Kitamura, R., Pendyala, R.M.: Comparative analysis of time-space prism vertices for out-of-home activity engagement on working and nonworking days. Environ. Plan. 31(2), 235–250 (2004)CrossRefGoogle Scholar
  74. Zhang, S., Tang, J., Wang, H., Wang, Y.: Enhancing traffic incident detection by using spatial point pattern analysis on social media. Transp. Res. Rec. J. Transp. Res. Board 2528, 69–77 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Civil and Environmental EngineeringUniversity of MarylandCollege ParkUSA
  2. 2.Department of Civil and Environmental EngineeringUniversity of MarylandCollege ParkUSA
  3. 3.Department of Environmental, Land and Infrastructure EngineeringPolitecnico di TorinoTurinItaly

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