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Investigating the nonlinear relationship between transportation system performance and daily activity–travel scheduling behaviour


  • Habib, Khandker Nurul
  • Sasic, Ana
  • Weis, Claude
  • Axhausen, Kay


The paper presents an econometric investigation of the behavioural relationship between transportation system performance in terms of travel time changes and daily activity–travel scheduling processes. Innovative survey data on the complete daily activity-scheduling adaptation process is used jointly with revealed scheduling information. The survey, conducted in Zurich, Switzerland, collected daily scheduling information together with stated adaptation responses corresponding to four adaptation scenarios. The four scenarios are defined by applying hypothetical increases in travel time of 50%, 100%, and 200% and a 50% decrease in travel time. Stated adaptation responses are collected in the context of 24-h activity scheduling. Data are used to estimate RUM based daily travel activity scheduling models. Models are estimated for one revealed schedule and four stated scheduling datasets. In addition, a joint model is estimated for pooled revealed and stated scheduling data. In the joint model, separate scale/variance parameters are estimated for revealed and stated information. Results clearly identify the nonlinear responses of activity–travel scheduling to the changes in travel time. Asymmetric responses are shown for travel time increases and decreases. People become more conservative with time expenditures when scheduling activities subject to increased travel times. However, beyond a certain limit of travel time increase, scheduling behaviour becomes more unpredictable. The lessons learned from this investigation have implications in the application of activity-based models for forecasting and policy analyses. Models developed using only a revealed preference dataset should not be used to extrapolate to situations where travel times changes by large margins. The results also prove that significant improvements in capturing behavioural responses in the activity scheduling process are possible by pooling revealed preference and stated preference data sets and jointly modelling with an explicit representation of RP scale/variance differences.

Suggested Citation

  • Habib, Khandker Nurul & Sasic, Ana & Weis, Claude & Axhausen, Kay, 2013. "Investigating the nonlinear relationship between transportation system performance and daily activity–travel scheduling behaviour," Transportation Research Part A: Policy and Practice, Elsevier, vol. 49(C), pages 342-357.
  • Handle: RePEc:eee:transa:v:49:y:2013:i:c:p:342-357
    DOI: 10.1016/j.tra.2013.01.016

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    References listed on IDEAS

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    2. Andrew Clark & Sean Doherty, 2010. "A multi-instrumented approach to observing the activity rescheduling decision process," Transportation, Springer, vol. 37(1), pages 165-181, January.
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    6. Khandker Habib & Eric Miller, 2008. "Modelling daily activity program generation considering within-day and day-to-day dynamics in activity-travel behaviour," Transportation, Springer, vol. 35(4), pages 467-484, July.
    7. Roorda, Matthew J. & Miller, Eric J. & Habib, Khandker M.N., 2008. "Validation of TASHA: A 24-h activity scheduling microsimulation model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(2), pages 360-375, February.
    8. Arentze, Theo & Hofman, Frank & Timmermans, Harry, 2004. "Predicting multi-faceted activity-travel adjustment strategies in response to possible congestion pricing scenarios using an Internet-based stated adaptation experiment," Transport Policy, Elsevier, vol. 11(1), pages 31-41, January.
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    Cited by:

    1. Helga Fehr-Duda & Robin Schimmelpfennig, 2018. "Wider die Zahlengläubigkeit: Sind Befragungsergebnisse eine gute Grundlage für wirtschaftspolitische Entscheidungen?," ECON - Working Papers 297, Department of Economics - University of Zurich, revised Dec 2018.
    2. Aboudina, Aya & Abdelgawad, Hossam & Abdulhai, Baher & Habib, Khandker Nurul, 2016. "Time-dependent congestion pricing system for large networks: Integrating departure time choice, dynamic traffic assignment and regional travel surveys in the Greater Toronto Area," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 411-430.
    3. Sarah Salem & Khandker M. Nurul Habib, 2019. "Use of repeated cross-sectional travel surveys for developing meta models of activity-travel scheduling processes," Transportation, Springer, vol. 46(2), pages 395-423, April.
    4. Clauss, Thomas & Döppe, Sebastian, 2016. "Why do urban travelers select multimodal travel options: A repertory grid analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 93(C), pages 93-116.
    5. Sharmeen, Fariya & Arentze, Theo & Timmermans, Harry, 2014. "An analysis of the dynamics of activity and travel needs in response to social network evolution and life-cycle events: A structural equation model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 59(C), pages 159-171.


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