IDEAS home Printed from https://ideas.repec.org/a/kap/transp/v51y2024i2d10.1007_s11116-022-10330-8.html
   My bibliography  Save this article

Estimating flexibility preferences to resolve temporal scheduling conflicts in activity-based modelling

Author

Listed:
  • Patrick Manser

    (Swiss Federal Railways (SBB))

  • Tom Haering

    (École Polytechnique Fédérale de Lausanne (EPFL), Transport and Mobility Laboratory (TRANSP-OR))

  • Tim Hillel

    (École Polytechnique Fédérale de Lausanne (EPFL), Transport and Mobility Laboratory (TRANSP-OR)
    University College London (UCL))

  • Janody Pougala

    (École Polytechnique Fédérale de Lausanne (EPFL), Transport and Mobility Laboratory (TRANSP-OR))

  • Rico Krueger

    (Technical University of Denmark (DTU))

  • Michel Bierlaire

    (École Polytechnique Fédérale de Lausanne (EPFL), Transport and Mobility Laboratory (TRANSP-OR))

Abstract

This paper presents a novel activity-based demand model that combines an optimisation framework for continuous temporal scheduling decisions (i.e. activity timings and durations) with traditional discrete choice models for non-temporal choice dimensions (i.e. activity participation, number and type of tours, and destinations). The central idea of our approach is that individuals resolve temporal scheduling conflicts that arise from overlapping activities, e.g. needing to work and desiring to shop at the same time, in order to maximise their daily utility. Flexibility parameters capture behavioural preferences that penalise deviations from desired timings. This framework has three advantages over existing activity-based modelling approaches: (i) the time conflicts between different temporal scheduling decisions including the activity sequence are treated jointly; (ii) flexibility parameters follow a utility maximisation approach; and (iii) the framework can be used to estimate and simulate a city-scale case study in reasonable time. We introduce an estimation routine that allows flexibility parameters to be estimated using real-world observations as well as a simulation routine to efficiently resolve temporal conflicts using an optimisation model. The framework is applied to the full-time workers of a synthetic population for the city of Lausanne, Switzerland. We validate the model results against reported schedules. The results demonstrate the capabilities of our approach to reproduce empirical observations in a real-world case study.

Suggested Citation

  • Patrick Manser & Tom Haering & Tim Hillel & Janody Pougala & Rico Krueger & Michel Bierlaire, 2024. "Estimating flexibility preferences to resolve temporal scheduling conflicts in activity-based modelling," Transportation, Springer, vol. 51(2), pages 501-528, April.
  • Handle: RePEc:kap:transp:v:51:y:2024:i:2:d:10.1007_s11116-022-10330-8
    DOI: 10.1007/s11116-022-10330-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11116-022-10330-8
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s11116-022-10330-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Soora Rasouli & Harry Timmermans, 2014. "Activity-based models of travel demand: promises, progress and prospects," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 18(1), pages 31-60, March.
    2. E. L. Lawler & D. E. Wood, 1966. "Branch-and-Bound Methods: A Survey," Operations Research, INFORMS, vol. 14(4), pages 699-719, August.
    3. Dimitrios Rizopoulos & Domokos Esztergár-Kiss, 2020. "A Method for the Optimization of Daily Activity Chains Including Electric Vehicles," Energies, MDPI, vol. 13(4), pages 1-21, February.
    4. Recker, Will W & Duan, J. & Wang, H., 2008. "Development of an estimation procedure for an activity-based travel demand model," University of California Transportation Center, Working Papers qt0rz778v6, University of California Transportation Center.
    5. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, September.
    6. Pougala, Janody & Hillel, Tim & Bierlaire, Michel, 2022. "Capturing trade-offs between daily scheduling choices," Journal of choice modelling, Elsevier, vol. 43(C).
    7. Mahmoud Javanmardi & Mehran Fasihozaman Langerudi & Ramin Shabanpour & Abolfazl Mohammadian, 2016. "An optimization approach to resolve activity scheduling conflicts in ADAPTS activity-based model," Transportation, Springer, vol. 43(6), pages 1023-1039, November.
    8. Surabhi Gupta & Peter Vovsha, 2013. "A model for work activity schedules with synchronization for multiple-worker households," Transportation, Springer, vol. 40(4), pages 827-845, July.
    9. MARCHAND, Hugues & MARTIN, Alexander & WEISMANTEL, Robert & WOLSEY, Laurence, 2002. "Cutting planes in integer and mixed integer programming," LIDAM Reprints CORE 1567, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Blom Västberg, Oskar & Karlström, Anders & Jonsson, Daniel & Sundberg, Marcus, 2016. "Including time in a travel demand model using dynamic discrete choice," MPRA Paper 75336, University Library of Munich, Germany, revised 11 Nov 2016.
    2. Weiss, Adam & Habib, Khandker Nurul, 2018. "A generalized parallel constrained choice model for intra-household escort decision of high school students," Transportation Research Part B: Methodological, Elsevier, vol. 114(C), pages 26-38.
    3. Xu, Zhiheng & Kang, Jee Eun & Chen, Roger, 2018. "A random utility based estimation framework for the household activity pattern problem," Transportation Research Part A: Policy and Practice, Elsevier, vol. 114(PB), pages 321-337.
    4. Mikołaj Czajkowski & Marek Giergiczny & Jakub Kronenberg & Jeffrey Englin, 2019. "The Individual Travel Cost Method with Consumer-Specific Values of Travel Time Savings," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 74(3), pages 961-984, November.
    5. Wang, Yihong & Correia, Gonçalo Homem de Almeida & de Romph, Erik & Timmermans, H.J.P., 2017. "Using metro smart card data to model location choice of after-work activities: An application to Shanghai," Journal of Transport Geography, Elsevier, vol. 63(C), pages 40-47.
    6. Lai, Xinjun & Lam, William H.K. & Su, Junbiao & Fu, Hui, 2019. "Modelling intra-household interactions in time-use and activity patterns of retired and dual-earner couples," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 172-194.
    7. Thuy Linh Hoang & Muhammad Adnan & Anh Tuan Vu & Nguyen Hoang-Tung & Bruno Kochan & Tom Bellemans, 2022. "Modeling and Structuring of Activity Scheduling Choices with Consideration of Intrazonal Tours: A Case Study of Motorcycle-Based Cities," Sustainability, MDPI, vol. 14(10), pages 1-23, May.
    8. Han, Bilin & Kim, Jinhee & Timmermans, Harry, 2023. "Work schedule arrangements in two-adult households with children," Transportation Research Part B: Methodological, Elsevier, vol. 177(C).
    9. Zhifeng Gao & Ted C. Schroeder, 2009. "Consumer responses to new food quality information: are some consumers more sensitive than others?," Agricultural Economics, International Association of Agricultural Economists, vol. 40(3), pages 339-346, May.
    10. Cheng, Leilei & Yin, Changbin & Chien, Hsiaoping, 2015. "Demand for milk quantity and safety in urban China: evidence from Beijing and Harbin," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 59(2), April.
    11. Johannes Buggle & Thierry Mayer & Seyhun Orcan Sakalli & Mathias Thoenig, 2023. "The Refugee’s Dilemma: Evidence from Jewish Migration out of Nazi Germany," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 138(2), pages 1273-1345.
    12. Christelis, Dimitris & Dobrescu, Loretti I. & Motta, Alberto, 2020. "Early life conditions and financial risk-taking in older age," The Journal of the Economics of Ageing, Elsevier, vol. 17(C).
    13. Ortega, David L. & Wang, H. Holly & Wu, Laping & Hong, Soo Jeong, 2015. "Retail channel and consumer demand for food quality in China," China Economic Review, Elsevier, vol. 36(C), pages 359-366.
    14. Doyle, Orla & Fidrmuc, Jan, 2006. "Who favors enlargement?: Determinants of support for EU membership in the candidate countries' referenda," European Journal of Political Economy, Elsevier, vol. 22(2), pages 520-543, June.
    15. Tovar, Jorge, 2012. "Consumers’ Welfare and Trade Liberalization: Evidence from the Car Industry in Colombia," World Development, Elsevier, vol. 40(4), pages 808-820.
    16. Pereira, Pedro & Ribeiro, Tiago, 2011. "The impact on broadband access to the Internet of the dual ownership of telephone and cable networks," International Journal of Industrial Organization, Elsevier, vol. 29(2), pages 283-293, March.
    17. Mark Morrison & Craig Nalder, 2009. "Willingness to Pay for Improved Quality of Electricity Supply Across Business Type and Location," The Energy Journal, , vol. 30(2), pages 117-134, April.
    18. Simon P. Anderson & André de Palma, 2012. "Competition for attention in the Information (overload) Age," RAND Journal of Economics, RAND Corporation, vol. 43(1), pages 1-25, March.
    19. Mtimet, Nadhem & Ujiie, Kiyokazu & Kashiwagi, Kenichi & Zaibet, Lokman & Nagaki, Masakazu, 2011. "The effects of Information and Country of Origin on Japanese Olive Oil Consumer Selection," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114642, European Association of Agricultural Economists.
    20. Chavez, Daniel E. & Palma, Marco A. & Nayga, Rodolfo M. & Mjelde, James W., 2020. "Product availability in discrete choice experiments with private goods," Journal of choice modelling, Elsevier, vol. 36(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:transp:v:51:y:2024:i:2:d:10.1007_s11116-022-10330-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.