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Agent-based model for continuous activity planning with an open planning horizon

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  • Fabian Märki
  • David Charypar
  • Kay Axhausen

Abstract

The paper proposes the microscopic travel demand model continuous target-based activity planning (C-TAP) that generates multi-week schedules by means of a continuous planning approach with an open planning horizon. C-TAP introduces behavioral targets to describe people’s motivation to perform activities, and it uses a planning heuristic to make on-the-fly decisions about upcoming activities. The planning heuristic bases its decisions on three aspects: a discomfort index derived from deviations from agents’ past performance with regard to their behavioral targets; the effectiveness of the immediate execution; and activity execution options available in the near future. The paper reports the results of a test scenario based on an existing 6-week continuous travel diary and validates C-TAP by comparing simulation results with observed behavioral patterns along several dimensions (weekday similarities, weekday execution probabilities of activities, transition probabilities between activities, duration distributions of activities, frequency distributions of activities, execution interval distributions of activities and weekly travel probability distributions). The results show that C-TAP has the capability to reproduce observed behavior and the flexibility to introduces new behavioral patterns. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Fabian Märki & David Charypar & Kay Axhausen, 2014. "Agent-based model for continuous activity planning with an open planning horizon," Transportation, Springer, vol. 41(4), pages 905-922, July.
    • Handle: RePEc:kap:transp:v:41:y:2014:i:4:p:905-922
    DOI: 10.1007/s11116-014-9512-y
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    References listed on IDEAS

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    1. Herbert A. Simon, 1955. "A Behavioral Model of Rational Choice," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 69(1), pages 99-118.
    2. 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.
    3. Arentze, Theo A. & Timmermans, Harry J.P., 2009. "A need-based model of multi-day, multi-person activity generation," Transportation Research Part B: Methodological, Elsevier, vol. 43(2), pages 251-265, February.
    4. G Ioannou & M Kritikos & G Prastacos, 2001. "A greedy look-ahead heuristic for the vehicle routing problem with time windows," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(5), pages 523-537, May.
    5. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
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    Cited by:

    1. Liu, Peng & Liao, Feixiong & Tian, Qiong & Huang, Hai-Jun & Timmermans, Harry, 2020. "Day-to-day needs-based activity-travel dynamics and equilibria in multi-state supernetworks," Transportation Research Part B: Methodological, Elsevier, vol. 132(C), pages 208-227.
    2. Zannat, Khatun E. & Laudan, Janek & Choudhury, Charisma F. & Hess, Stephane, 2024. "Developing an agent-based microsimulation for predicting the Bus Rapid Transit (BRT) demand in developing countries: A case study of Dhaka, Bangladesh," Transport Policy, Elsevier, vol. 148(C), pages 92-106.
    3. 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.
    4. Biruk Gebremedhin Mesfin & Zihao Li & Daniel (Jian) Sun & Deming Chen & Yueting Xi, 2024. "Urban traffic-parking system dynamics model with macroscopic properties: a comparative study between Shanghai and Zurich," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.

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