, Volume 41, Issue 5, pp 995–1011 | Cite as

Repetitions in individual daily activity–travel–location patterns: a study using the Herfindahl–Hirschman Index



Using Herfindahl–Hirschman Index and the Mobidrive and Thurgau six-week travel diary datasets this paper examines the degree of repetition of individuals’ choices of their daily activity–travel–location combinations. The results show that the repetitiveness of individual activity–travel–mode–location combinations is highly influenced by the individuals’ out-of-home commitments, the intra-household conditions and the availability and the accessibility of the activity locations. Different types of activity have different pattern of repetition. The level of repetition of individual’s daily activity–travel pattern is less correlated to travel mode choice, but more to the individuals’ commitments and obligations. The repetitiveness of mode choices is more related to the conditions or the accessibilities of the activity location, but not directly to the activity itself.


Individual spatial–travel behaviour variability Activity–travel–mode–location combination Herfindahl–Hirschman Index Six-week travel diary data 



A previous version of the paper was presented at the 11th World Conference of Transport Research, Berkeley, June 2007.


  1. Axhausen, K.W., Löchl, M., Schlich, R., Buhl, T., Widmer, P.: Fatigue in long-duration travel diaries. Transportation 34(2), 143–160 (2007)CrossRefGoogle Scholar
  2. Axhausen, K.W., Zimmermann, A., Schönfelder, S., Rindsfüser, G., Haupt, T.: Observing the rhythms of daily life: a six-week travel diary. Transportation 29(2), 95–124 (2002)CrossRefGoogle Scholar
  3. 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, 193–209 (1999)CrossRefGoogle Scholar
  4. Buliung, R.N., Roorda, M.J.: Spatial variety in weekly, weekday-to-weekend, and day-to-day patterns of activity-travel behaviour: initial result from the Toronto travel-activity panel survey. Paper presented at the 85th annual transportation research board meeting, Washington (2006)Google Scholar
  5. Buliung, R.N., Roorda, M.J., Remmel, T.K.: Exploring spatial variety in patterns of activity-travel behaviour: initial results from the Toronto Travel-Activity Panel Survey. Transportation 35, 697–722 (2008)CrossRefGoogle Scholar
  6. Cesari, R.: A generalized measure of competition. Appl. Econ. Lett. 7, 479–481 (2000)CrossRefGoogle Scholar
  7. Cowling, K., Waterson, M.: Price cost margins and industry structure. Econ. J. 43, 267–274 (1976a)Google Scholar
  8. Chikaraishi, M., Fujiwara, A., Zhang, J., Axhausen, K.W.: Exploring variation properties of departure time choice behavior using a multilevel analysis approach. Transp. Res. Rec. 2134, 10–20 (2009)CrossRefGoogle Scholar
  9. Chikaraishi, M., Fujiwara, A., Zhang, J., Axhausen, K.W., Zumkeller, D.: Changes in variations of travel time expenditure: some methodological considerations and empirical results from German mobility panel. Transp. Res. Rec. 2230, 121–131 (2011a)CrossRefGoogle Scholar
  10. Chikaraishi, M., Zhang, J., Fujiwara, A., Axhausen, K.W.: Exploring variation properties of time use behavior based on a Multilevel Multiple Discrete-Continuous Extreme Value model. Transp. Res. Rec. 2156, 101–110 (2010)CrossRefGoogle Scholar
  11. Chikaraishi, M., Zhang, J., Fujiwara, A., Axhausen, K.W.: Identifying variations and co-variations in discrete choice models. Transportation 38(6), 993–1016 (2011b)CrossRefGoogle Scholar
  12. Cowling, K., Waterson, M.: Price cost margins and industry structure. Econ. J. 43, 267–274 (1976b)Google Scholar
  13. Dharmowijoyo, D.B.E., Susilo, Y.O., Karlström, A., 2013, The day-to-day inter and intra personal variability of individual’s activity space in developing country, Forthcoming at a special issue at the Environment and Planning BGoogle Scholar
  14. Hägerstrand, T.: What about people in regional science? Pap. Reg. Sci. Assoc. 24, 7–21 (1970)CrossRefGoogle Scholar
  15. Hanson, S.: Spatial diversification and multipurpose travel: implications for choice theory. Geogr. Anal. 12(3), 245–257 (1980)CrossRefGoogle Scholar
  16. Hanson, S., Huff, J.O.: Systematic variability in repetitious travel. Transportation 15, 111–135 (1988)Google Scholar
  17. Herfindahl, O.C.: Concentration in the steel industry. Ph.D. Thesis, Columbia University (1950)Google Scholar
  18. Herfindahl, O.C.: Concentration in the steel industry. Unpublished PhD Dissertation, Columbia University (1950)Google Scholar
  19. Horton, F., Reynolds, D.R.: Effects of urban spatial structure on individual behaviour. Econ. Geogr. 47, 36–48 (1971)CrossRefGoogle Scholar
  20. Huff, J.O., Hanson, S.: Measurement of habitual behaviour: Examining systematic variability in repetitive travel. In: Jones, P. (ed.) Developments in Dynamic and Activity-Based Approaches to Travel Analysis, pp. 229–249. Gower, Aldershot (1990)Google Scholar
  21. Joh, C.-H., Arentze, T.A., Timmermans, H.P.: Pattern recognition in complex activity travel patterns: comparison of Euclidean distance, signal-processing theoretical, and multidimensional sequence alignment methods’. Transp. Res. Rec. 1752, 16–31 (2001a)CrossRefGoogle Scholar
  22. Joh, C.-H., Arentze, T.A., Timmermans, H.P.: A position-sensitive sequence-alignment method illustrated for space–time activity-diary data. Environ. Plan. A 33, 313–338 (2001b)CrossRefGoogle Scholar
  23. Joh, C.H., Arentze, T.A., Hofman, F., Timmermans, H.J.P.: Activity-travel pattern similarity: a multidimensional alignment method. Transp. Res. B 36, 385–403 (2002)CrossRefGoogle Scholar
  24. Joh, C.H., Timmermans, H.J.P.: Applying sequential alignment methods to large activity-travel data sets: exploration of a heuristic approach. Transp. Res. Rec. 2231, 10–17 (2011)CrossRefGoogle Scholar
  25. Kang, H., Scott, D.M.: Exploring day-to-day variability in time use for household members. Transp. Res. Part A 44, 609–619 (2010)CrossRefGoogle Scholar
  26. Kitamura, R.: An analysis of weekly activity patterns and travel expendi-ture. In: Golledge, R.G., Timmermans, H.J.P. (eds.) Behavioral Modeling Approaches in Geography and Planning, pp. 399–423. Croom Helm, London (1988)Google Scholar
  27. 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. A 40(3), 259–279 (2006)Google Scholar
  28. Kwoka Jr, J.E.: Herfindahl concentration with an import fringe and with supply constraints. Rev. Ind. Organ. 13, 401–407 (1998)CrossRefGoogle Scholar
  29. Lenntorp, B.: Paths in space–time environment: a time geographic study of possibilities of individuals. Lund Stud. Geogr. B 44 (1976)Google Scholar
  30. Lijesen, M.: Adjusting the Herfindahl index for close substitutes: an application to pricing in civil aviation. Transp. Res. Part E 40, 123–134 (2004)CrossRefGoogle Scholar
  31. Lijesen, M.G., Nijkamp, P., Rietveld, P.: Measuring competition in civil aviation. J. Air Transp. Manag. 8, 189–197 (2002)CrossRefGoogle Scholar
  32. Löchl, M., Axhausen, K.W., Schönfelder, S.: Analysing Swiss longitudinal travel data. Paper presented at the 5th Swiss Transport Research Conference, Ascona, March 2005Google Scholar
  33. Marble, D.F., Bowbly, S.R.: Shopping alternatives and recurrent travel patterns. In: Horton, F. (ed.) Geographic studies of urban transportation and network analysis, pp. 42–75. Northwestern University Press, Evanston (1968)Google Scholar
  34. Pas, E.I.: Intrapersonal variability and model of goodness-of-fit. Transp. Res. A 21, 431–438 (1987)CrossRefGoogle Scholar
  35. Pas, E.I., Koppelman, F.S.: An examination of the determinants of day-to-day variability in individuals’ travel behavior. Transportation 13, 183–200 (1986)CrossRefGoogle Scholar
  36. Pas, E.I., Sundar, S.: Intrapersonal variability in daily urban travel behavior: some additional evidence. Transportation 24, 1–16 (1994)Google Scholar
  37. Schlich, R., Axhausen, K.W.: Habitual travel behaviour: evidence from a six-week travel diary. Transportation 30, 13–36 (2003)CrossRefGoogle Scholar
  38. Schönfelder, S., Axhausen, K.W.: Mobidrive—Längsschnitterhebungen zum individuellen verkehrsverhalten: perspektiven für raum-zeitliche analysen. Paper presented at CORP 2001, Wien, February 2001Google Scholar
  39. Susilo, Y.O., Kitamura, R.: Structural changes in commuters’ daily travel: the case of auto and transit commuters in the Osaka metropolitan area of Japan, 1980 through 2000. Transp. Res. A 42, 95–115 (2008)Google Scholar
  40. Susilo, Y.O., Dijst, M.: Behavioural decisions of travel–time ratio for work, maintenance and leisure activities in the Netherlands. J. Transp. Plan. Technol. 33, 19–34 (2010)CrossRefGoogle Scholar
  41. Susilo, Y.O., Kitamura, R.: Analysis of the day-to-day variability in the individual’s action space: an exploration of the six-week Mobidrive travel diary data. Transp. Res. Rec. 1902, 124–133 (2005)CrossRefGoogle Scholar
  42. Wilson, W.C.: Activity pattern analysis by mans of sequence-alignment methods. Environ. Plan. A 30, 1017–1038 (1998)CrossRefGoogle Scholar
  43. Yamamoto, T., Kitamura, R.: An analysis of time allocation to in-home and out-of-home discretionary activities across working days and no-working days. Transportation 26, 211–230 (1999)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Transport ScienceRoyal Institute of Technology (KTH)StockholmSweden
  2. 2.IVTETH ZürichZurichSwitzerland

Personalised recommendations