IDEAS home Printed from https://ideas.repec.org/a/kap/transp/v47y2020i5d10.1007_s11116-019-10003-z.html
   My bibliography  Save this article

Investigating the influence of assigning a higher priority to scheduling work and school activities in the activity-based models on the simulated travel/activity patterns

Author

Listed:
  • Leila Dianat

    (IBI Group)

  • Khandker Nurul Habib

    (University of Toronto)

  • Eric J. Miller

    (University of Toronto)

Abstract

Two dynamic, gap-based activity scheduling models are tested by applying a short-run microsimulation approach to replicate workers’ travel/activity patterns over a 1-week time period. In the first model, a two-level work episode scheduling model is applied to schedule weekly work episodes (Dianat et al. in Transp Res Rec 2664:59–68, 2017. https://doi.org/10.3141/2664-07 ). This includes joint choices of working or not on each day and work episode duration and start time in case of choosing to work. Assigning higher priority to scheduling work episodes, and assuming night sleep to be pre-determined, provides a weekly “skeleton schedule”. Non-work/school (NWS) episodes are then generated and scheduled in the available gaps as a joint choice of activity type and destination followed by a continuous time expenditure choice. The second model applies the same mathematical framework as the NWS model for scheduling all activity types including work/school, considering only night sleep as the pre-determined skeleton schedule. This exercise allows us to study the impact of assigning a higher priority to scheduling work/school activities on complete out-of-home travel/activity pattern prediction, compared to the alternative hypothesis, which is scheduling all the activities simultaneously. Comparing the simulation outcomes of the two models with the observed dataset reveals that organizing NWS episodes around the schedule skeleton not only is behaviorally more representative but also increases the accuracy of the predicted NWS episodes’ patterns. Moreover, applying the work scheduling model results in a more accurate prediction of the weekly work schedule compared to the second model.

Suggested Citation

  • Leila Dianat & Khandker Nurul Habib & Eric J. Miller, 2020. "Investigating the influence of assigning a higher priority to scheduling work and school activities in the activity-based models on the simulated travel/activity patterns," Transportation, Springer, vol. 47(5), pages 2109-2132, October.
    • Handle: RePEc:kap:transp:v:47:y:2020:i:5:d:10.1007_s11116-019-10003-z
    DOI: 10.1007/s11116-019-10003-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11116-019-10003-z
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11116-019-10003-z?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. Langerudi, Mehran Fasihozaman & Javanmardi, Mahmoud & Shabanpour, Ramin & Rashidi, Taha Hossein & Mohammadian, Abolfazl, 2017. "Incorporating in-home activities in ADAPTS activity-based framework: A sequential conditional probability approach," Journal of Transport Geography, Elsevier, vol. 61(C), pages 48-60.
    2. Sean Doherty & Eric Miller, 2000. "A computerized household activity scheduling survey," Transportation, Springer, vol. 27(1), pages 75-97, February.
    3. Arentze, Theo A. & Timmermans, Harry J. P., 2004. "A learning-based transportation oriented simulation system," Transportation Research Part B: Methodological, Elsevier, vol. 38(7), pages 613-633, August.
    4. Auld, Joshua & Mohammadian, Abolfazl(Kouros), 2012. "Activity planning processes in the Agent-based Dynamic Activity Planning and Travel Scheduling (ADAPTS) model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(8), pages 1386-1403.
    5. Rajesh Paleti & Peter Vovsha & Gaurav Vyas & Rebekah Anderson & Gregory Giaimo, 2017. "Activity sequencing, location, and formation of individual non-mandatory tours: application to the activity-based models for Columbus, Cincinnati, and Cleveland, OH," Transportation, Springer, vol. 44(3), pages 615-640, May.
    6. Khandker Habib, 2011. "A random utility maximization (RUM) based dynamic activity scheduling model: Application in weekend activity scheduling," Transportation, Springer, vol. 38(1), pages 123-151, January.
    7. Bhat, Chandra R., 2008. "The multiple discrete-continuous extreme value (MDCEV) model: Role of utility function parameters, identification considerations, and model extensions," Transportation Research Part B: Methodological, Elsevier, vol. 42(3), pages 274-303, March.
    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. Dianat, Leila & Habib, Khandker Nurul & Miller, Eric J., 2020. "Modeling and forecasting daily non-work/school activity patterns in an activity-based model using skeleton schedule constraints," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 337-352.
    2. Ozonder, Gozde & Miller, Eric J., 2021. "Longitudinal investigation of skeletal activity episode timing decisions – A copula approach," Journal of choice modelling, Elsevier, vol. 40(C).
    3. Sheila Ferrer & Tomás Ruiz, 2017. "Comparison on travel scheduling between driving and walking trips by habitual car users," Transportation, Springer, vol. 44(1), pages 27-48, January.
    4. 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.
    5. Hasnine, Md Sami & Habib, Khandker Nurul, 2018. "What about the dynamics in daily travel mode choices? A dynamic discrete choice approach for tour-based mode choice modelling," Transport Policy, Elsevier, vol. 71(C), pages 70-80.
    6. Yasmin, Farhana & Morency, Catherine & Roorda, Matthew J., 2015. "Assessment of spatial transferability of an activity-based model, TASHA," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 200-213.
    7. Yoon, Seo Youn & Ravulaparthy, Srinath K. & Goulias, Konstadinos G., 2014. "Dynamic diurnal social taxonomy of urban environments using data from a geocoded time use activity-travel diary and point-based business establishment inventory," Transportation Research Part A: Policy and Practice, Elsevier, vol. 68(C), pages 3-17.
    8. André de Palma & Nathalie Picard & Robin Lindsey, 2024. "Activity and transportation decisions within households," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 16, pages 426-451, Edward Elgar Publishing.
    9. Dane, Gamze & Arentze, Theo A. & Timmermans, Harry J.P. & Ettema, Dick, 2014. "Simultaneous modeling of individuals’ duration and expenditure decisions in out-of-home leisure activities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 70(C), pages 93-103.
    10. Chandra Bhat & Konstadinos Goulias & Ram Pendyala & Rajesh Paleti & Raghuprasad Sidharthan & Laura Schmitt & Hsi-Hwa Hu, 2013. "A household-level activity pattern generation model with an application for Southern California," Transportation, Springer, vol. 40(5), pages 1063-1086, September.
    11. Vo, Khoa D. & Lam, William H.K. & Chen, Anthony & Shao, Hu, 2020. "A household optimum utility approach for modeling joint activity-travel choices in congested road networks," Transportation Research Part B: Methodological, Elsevier, vol. 134(C), pages 93-125.
    12. Ali Najmi & Taha H. Rashidi & James Vaughan & Eric J. Miller, 2020. "Calibration of large-scale transport planning models: a structured approach," Transportation, Springer, vol. 47(4), pages 1867-1905, August.
    13. 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.
    14. Konstadinos G. Goulias & Ram M. Pendyala, 2014. "Choice context," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 5, pages 101-130, Edward Elgar Publishing.
    15. Pinjari, Abdul Rawoof & Bhat, Chandra, 2010. "A multiple discrete-continuous nested extreme value (MDCNEV) model: Formulation and application to non-worker activity time-use and timing behavior on weekdays," Transportation Research Part B: Methodological, Elsevier, vol. 44(4), pages 562-583, May.
    16. Langerudi, Mehran Fasihozaman & Javanmardi, Mahmoud & Shabanpour, Ramin & Rashidi, Taha Hossein & Mohammadian, Abolfazl, 2017. "Incorporating in-home activities in ADAPTS activity-based framework: A sequential conditional probability approach," Journal of Transport Geography, Elsevier, vol. 61(C), pages 48-60.
    17. Khandker Habib, 2011. "A random utility maximization (RUM) based dynamic activity scheduling model: Application in weekend activity scheduling," Transportation, Springer, vol. 38(1), pages 123-151, January.
    18. Tomás Ruiz & Harry Timmermans, 2006. "Changing the timing of activities in resolving Scheduling Conflicts," Transportation, Springer, vol. 33(5), pages 429-445, September.
    19. Lee-Gosselin, Martin & Miranda-Moreno, Luis F., 2009. "What is different about urban activities of those with access to ICTs? Some early evidence from Québec, Canada," Journal of Transport Geography, Elsevier, vol. 17(2), pages 104-114.
    20. Shaheen, Susan A. & Wright, John, 2001. "Research Approach and Early Findings," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt8f8803dx, Institute of Transportation Studies, UC Berkeley.

    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:47:y:2020:i:5:d:10.1007_s11116-019-10003-z. 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.