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A Flexible and Integrated Methodology for Analytical Classification of Daily Travel-Activity Behavior

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  • Eric I. Pas

    (Duke University, Durham, North Carolina)

Abstract

The importance of incorporating the derived demand nature of urban person movement and the interdependence of the elemental travel episodes (trips) in analyses of urban travel behavior is discussed. A flexible and integrated approach to the analysis of daily urban travel-activity behavior as a complex phenomenon is described. The methodology incorporates systematic identification of classes of similar daily travel-activity patterns and the evaluation and interpretation of these groups. The approach described here comprises three stages; namely, transformation, group formation, and cluster interpretation and evaluation. In the first stage, raw input data describing the daily travel-activity patterns of a sample of individuals is transformed into a set of points in a real Euclidean space, where each point represents a daily travel-activity pattern. In the second stage, a cluster analysis algorithm is employed to identify groups of similar daily travel-activity patterns. In the third stage, the identified groups are interpreted by defining representative patterns for each group. Classifications produced by the methodology can be used to analyze and model relationships between daily travel-activity behavior and potential explanatory variables.

Suggested Citation

  • Eric I. Pas, 1983. "A Flexible and Integrated Methodology for Analytical Classification of Daily Travel-Activity Behavior," Transportation Science, INFORMS, vol. 17(4), pages 405-429, November.
    • Handle: RePEc:inm:ortrsc:v:17:y:1983:i:4:p:405-429
    DOI: 10.1287/trsc.17.4.405
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    Cited by:

    1. Ron Buliung & Matthew Roorda & Tarmo Remmel, 2008. "Exploring spatial variety in patterns of activity-travel behaviour: initial results from the Toronto Travel-Activity Panel Survey (TTAPS)," Transportation, Springer, vol. 35(6), pages 697-722, November.
    2. Erika Spissu & Abdul Pinjari & Chandra Bhat & Ram Pendyala & Kay Axhausen, 2009. "An analysis of weekly out-of-home discretionary activity participation and time-use behavior," Transportation, Springer, vol. 36(5), pages 483-510, September.
    3. Jara-Díaz, Sergio & Rosales-Salas, Jorge, 2015. "Understanding time use: Daily or weekly data?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 76(C), pages 38-57.
    4. E I Pas, 1984. "The Effect of Selected Sociodemographic Characteristics on Daily Travel-Activity Behavior," Environment and Planning A, , vol. 16(5), pages 571-581, May.
    5. D G Janelle & M F Goodchild & B Klinkenberg, 1988. "Space-Time Diaries and Travel Characteristics for Different Levels of Respondent Aggregation," Environment and Planning A, , vol. 20(7), pages 891-906, July.
    6. Joh, Chang-Hyeon & Arentze, Theo & Hofman, Frank & Timmermans, Harry, 2002. "Activity pattern similarity: a multidimensional sequence alignment method," Transportation Research Part B: Methodological, Elsevier, vol. 36(5), pages 385-403, June.
    7. Rafiq, Rezwana & McNally, Michael G., 2021. "Heterogeneity in Activity-travel Patterns of Public Transit Users: An Application of Latent Class Analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 152(C), pages 1-18.
    8. Dharmowijoyo, Dimas B.E. & Susilo, Yusak O. & Karlström, Anders, 2017. "Analysing the complexity of day-to-day individual activity-travel patterns using a multidimensional sequence alignment model: A case study in the Bandung Metropolitan Area, Indonesia," Journal of Transport Geography, Elsevier, vol. 64(C), pages 1-12.
    9. Bhat, Chandra R. & Frusti, Teresa & Zhao, Huimin & Schönfelder, Stefan & Axhausen, Kay W., 2004. "Intershopping duration: an analysis using multiweek data," Transportation Research Part B: Methodological, Elsevier, vol. 38(1), pages 39-60, January.
    10. Neutens, Tijs & Delafontaine, Matthias & Scott, Darren M. & De Maeyer, Philippe, 2012. "An analysis of day-to-day variations in individual space–time accessibility," Journal of Transport Geography, Elsevier, vol. 23(C), pages 81-91.
    11. Marlies Vanhulsel & Carolien Beckx & Davy Janssens & Koen Vanhoof & Geert Wets, 2011. "Measuring dissimilarity of geographically dispersed space–time paths," Transportation, Springer, vol. 38(1), pages 65-79, January.
    12. Ciscal-Terry, Wilner & Dell'Amico, Mauro & Hadjidimitriou, Natalia Selini & Iori, Manuel, 2016. "An analysis of drivers route choice behaviour using GPS data and optimal alternatives," Journal of Transport Geography, Elsevier, vol. 51(C), pages 119-129.
    13. Siyu Li & Der-Horng Lee, 2017. "Learning daily activity patterns with probabilistic grammars," Transportation, Springer, vol. 44(1), pages 49-68, January.
    14. Kay Axhausen & Andrea Zimmermann & Stefan Schönfelder & Guido Rindsfüser & Thomas Haupt, 2002. "Observing the rhythms of daily life: A six-week travel diary," Transportation, Springer, vol. 29(2), pages 95-124, May.
    15. Zhai, Wei & Bai, Xueyin & Peng, Zhong-ren & Gu, Chaolin, 2019. "From edit distance to augmented space-time-weighted edit distance: Detecting and clustering patterns of human activities in Puget Sound region," Journal of Transport Geography, Elsevier, vol. 78(C), pages 41-55.
    16. Elisabetta Cherchi & Cinzia Cirillo, 2014. "Understanding variability, habit and the effect of long period activity plan in modal choices: a day to day, week to week analysis on panel data," Transportation, Springer, vol. 41(6), pages 1245-1262, November.
    17. Morency, Catherine & Trépanier, Martin & Agard, Bruno, 2007. "Measuring transit use variability with smart-card data," Transport Policy, Elsevier, vol. 14(3), pages 193-203, May.
    18. Joh, Chang-Hyeon & Arentze, Theo A. & Timmermans, Harry J. P., 1999. "Multidimensional Sequence Alignment Methods for Activity Pattern Analysis: A comparison of dynamic programming and genetic algorithms," ERSA conference papers ersa99pa279, European Regional Science Association.

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