Skip to main content
Log in

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

  • Published:
Transportation Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Arentze, T.A., Hofman, F., Mourik, H., Timmermans, H.J.P.: Albatross: a multi-agent rule-based model of activity pattern decisions. Transp. Res. Rec. 1706, 136–144 (2000)

    Article  Google Scholar 

  • Arentze, T.A., Timmermans, H.J.P.: A new theory of dynamic activity generation. Paper presented at the 85th annual meeting of the Transportation Research Board, Washington, D.C., Jan 2006

  • Arentze, T.A., Timmermans, H.J.P.: Modelling dynamics of activity–travel behaviour. Paper presented at the 12th international conference of Hong Kong Society for Transportation Studies, Hong Kong, Dec 2007

  • Arentze, T.A., Timmermans, H.J.P.: A need-based model of multi-day, multi-person activity generation. Transp. Res. Part B 43(2), 251–265 (2009)

    Article  Google Scholar 

  • Atkinson, J.B.: A greedy look-ahead heuristic for combinatorial optimization: an application to vehicle scheduling with time windows. J. Oper. Res. Soc. 45(6), 673–684 (1994)

    Article  Google Scholar 

  • Axhausen, K.W.: Judging the day: a synthesis of the literature on measuring the utility of activity patterns. Working Paper, 561, Transport Studies Unit. University of Oxford, Oxford (1990a)

  • Axhausen, K.W.: A simultaneous simulation of activity chains and traffic flow. In: Jones, P.M. (eds) Developments in Dynamic and Activity-Based Approaches to Travel Analysis., pp. 206–225. Avebury, Aldershot (1990)

    Google Scholar 

  • Axhausen, K.W., Löchl, M., Schlich, R.: Fatigue in long duration surveys. Transportation 34(2), 143–160 (2007)

    Article  Google Scholar 

  • Axhausen, K.W., Zimmermann, A., Schönfelder, S., Rindsfüser, G., Haupt, T.: Observing the rhythms of daily life: a six-week travel diary. Transportation 29(2), 95–124 (2002)

    Article  Google Scholar 

  • Balmer, M.: Travel demand modeling for multi-agent traffic simulations: algorithms and systems. Ph.D. Thesis, ETH Zurich, Zurich, May 2007

  • Bhat, C.R., Guo, J.Y., Srinivasan, S., Sivakumar, A.: A comprehensive econometric microsimulator for daily activity–travel patterns (CEMDAP). Transp. Res. Rec. 1894, 57–66 (2004)

    Article  Google Scholar 

  • Bowman, J.L.: The day activity schedule approach to travel demand analysis. Ph.D. Thesis, Massachusetts Institute of Technology, Cambridge (1998)

  • Charypar, D., Horni, A., Axhausen, K.W.: Need-based activity planning in an agent-based environment. Paper presented at the 12th international conference on Travel Behaviour Research (IATBR), Jaipur, Dec 2009

  • Charypar, D., Nagel, K.: Q-learning for flexible learning of daily activity plans. Transp. Res. Rec. 1935, 163–169 (2006)

    Article  Google Scholar 

  • Chikaraishi, M., Zhang, J., Fujiwara, A., Axhausen, K.W.: Exploring variation properties of time use behavior based on a multilevel multiple discrete-continuous extreme value model. Transp. Res. Rec. 2156, 101–110 (2010)

    Article  Google Scholar 

  • Dobler, C., Kowald, M., Schüssler, N., Axhausen, K.W.: Within-day replanning of exceptional events. Paper presented at the 91st annual meeting of the Transportation Research Board, Washington, D.C., January 2012

  • Doherty, S.T.: How far in advance are activities planned?. Measurement challenges and analysis, Transp. Res. Rec. 1926, 41–49 (2005)

    Google Scholar 

  • Gliebe, J.P., Kim, K.: Time-dependent utility in activity and travel choice behavior. Transp. Res. Rec. 2156, 9–16 (2010)

    Article  Google Scholar 

  • Horni, A., Nagel, K., Axhausen K.W.: High-resolution destination choice in agent-based demand models. Paper presented at the 91st annual meeting of the Transportation Research Board, Washington, D.C., January 2012

  • Ioannou, G., Kritikos, M., Prastacos, G.: A greedy look-ahead heuristic for the vehicle routing problem with time windows. J. Oper. Res. Soc. 52(5), 523–537 (2001)

    Article  Google Scholar 

  • Joh, C.-H.: Measuring and predicting adaptation in multidimensional activity–travel patterns. Ph.D. Thesis, Technical University Eindhoven, Eindhoven (2004)

  • Kuhnimhof, T., Gringmuth, C.: Multiday multiagent model of travel behavior with activity scheduling. Transp. Res. Rec. 2134, 178–185 (2009)

    Article  Google Scholar 

  • Märki, F., Charypar, D., Axhausen, K.W.: Continuous activity planning for a continuous traffic simulation. Transp. Res. Rec. 2230, 29–37 (2011)

    Article  Google Scholar 

  • Märki, F., Charypar, D., Axhausen, K.W.: Location choice in a continuous model. Paper presented at the 13th international conference on Travel Behaviour Research (IATBR), Toronto, July 2012

  • Märki, F., Charypar, D., Axhausen K.W.: Integration of household interaction with a continuous simulation model. Paper presented at the 13th Swiss Transport Research Conference, Ascona, April 2013

  • McFadden, D., Train, K.E.: Mixed MNL models for discrete response. J. Appl. Econom. 15(5), 447–470 (2000)

    Article  Google Scholar 

  • Miller, E.J., Roorda, M.J.: A prototype model of 24-h household activity scheduling for the Toronto area. Transp. Res. Rec. 1831, 114–121 (2003)

    Article  Google Scholar 

  • Müller, K., Axhausen, K.W.: Hierarchical IPF: Generating a synthetic population for Switzerland. Paper presented at the 51st congress of the European Regional Science Association, Barcelona, September 2011

  • Müller, K., Axhausen, K.W.: Preparing the Swiss Public-Use Sample for generating a synthetic population of Switzerland. Paper presented at the 12th Swiss Transport Research Conference, Ascona, May 2012

  • Nagel, K., Flötteröd, G.: Agent-based traffic assignment: Going from trips to behavioral travelers. Paper presented at the 12th international conference on Travel Behaviour Research (IATBR), Jaipur, December 2009

  • Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. Int. J. Comput. Vis. 40(2), 99–121 (2000)

    Article  Google Scholar 

  • Schlich, R.: Verhaltenshomogene Gruppen in Längsschnitterhebungen. Ph.D. Thesis, ETH Zurich, Zurich, April 2004

  • Schnittger, S., Zumkeller D.: Longitudinal microsimulation as a tool to merge transport planning and traffic engineering models: the MobiTopp model. Paper presented at the European Transport Conference, Strasbourg, Oct 2004

  • Schönfelder, S.: Urban rhythms: modelling the rhythms of individual travel behaviour. Ph.D. Thesis, ETH Zurich, Zurich (2006)

  • Simon, H.: A behavioral model of rational choice. Quart. J. Econ. 69(1), 99–118 (1955)

    Article  Google Scholar 

  • Smith, L., Beckman, R.J., Anson, D., Nagel, K., Williams, M.E.: TRANSIMS: transportation analysis and simulation system. Paper presented at the 5th TRB national transportation planning methods applications conference, Seattle, April 1995

  • Swiss Federal Statistical Office (BFS): Ergebnisse des Mikrozensus 2005 zum Verkehrsverhalten, Swiss Federal Statistical Office (BFS), Neuchatel (2006)

  • Winston, G.C.: The Timing of Economic Activities. Cambridge University Press, Cambridge (1982)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fabian Märki.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Märki, F., Charypar, D. & Axhausen, K.W. Agent-based model for continuous activity planning with an open planning horizon. Transportation 41, 905–922 (2014). https://doi.org/10.1007/s11116-014-9512-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11116-014-9512-y

Keywords

Navigation