Puma: Multi-Agent Modelling of Urban Systems

  • D. Ettema
  • K. de Jong
  • H. Timmermans
  • A. Bakema
Part of the The GeoJournal Library book series (GEJL, volume 90)


It is increasingly recognised that land-use change processes are the outcome of decisions made by individual actors, such as land owners, authorities, firms and households. In order to improve the theoretical basis of land-use modelling and to represent land-use changes in a behaviourally more realistic way, we are developing PUMA (Predicting Urbanisation with Multi-Agents), a fully fledged multi-agent system of urban processes. PUMAwill consist of various modules, representing the behaviours of specific actors. The land conversion module describes farmers’, authorities’, investors’ and developers’ decisions to sell or buy land and develop it into other uses. The households module describes households’ housing and work careers in relation to life cycle events (marriage, child birth, ageing, job change et cetera.) and also their daily activity patterns. The firms module includes firms’ demography and their related demand for production facilities leading to (re)location processes. The chapter describes the conceptual model, the first phase of operationalisation and initial results.


Multi-agent models integrated land-use transportation models. 


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  1. Arentze, T.A. and Timmermans, H.J.P. (2005) A cognitive agent-based micro-simulation framework for dynamic activity-travel scheduling decisions, Paper presented at the Knowledge, Planning and Spatial AnalysisConference, Pisa, June.Google Scholar
  2. Batty, M. (2005) Agents, cells and cities: new representational models for simulating multi-scale urban dynamics, Environment and Planning A 37, 8: 1373–1394.CrossRefGoogle Scholar
  3. Benenson, I., Omer, I. and Hatna, E. (2002) Entity-based modeling of urban residential dynamics: the case of Yaffo, Tel Aviv, Environment and Planning B, 29: 491–512.CrossRefGoogle Scholar
  4. De Bok, M. and Sanders, F. (2005) Modeling firms’ location behaviour, Paper presented at the 2005 TRB Annual Meeting, Washington D.C.Google Scholar
  5. Devisch, O., Arentze, T.A. Borgers, A.W.J. and Timmermans, H.J.P. (2005) An agent-based model of residential choice dynamics in non-stationary housing markets, Paper CUPUM Conference, London.Google Scholar
  6. Hunt, J.D., Abraham, J.E. and Weidner, T. (2004) The land development module of the Oregon2 modeling framework, Paper presented at the 2004 TRB Annual Meeting, Washington D.C.Google Scholar
  7. Joh, C.-H., Arentze, T.A. and Timmermans, H.J.P. (2003) Understanding activity scheduling and rescheduling behaviour: theory and numerical simulation, in Boots B., et al. (eds) Modelling Geographical Systems, Kluwer Academic Publishers, Dordrecht, pp. 73–95.Google Scholar
  8. Lowry, I.S. (1963) Location parameters in the Pittsburgh model, Papers and Proceedings of the Regional Science Association, 11: 145–165.CrossRefGoogle Scholar
  9. Mathevet, R., Bousquet, F., Le Page, C. and Antona, M. (2003) Agent-based simulations of interactions between duck population, farming decisions and leasing of hunting rights in the Camargue (Southern France), Ecological Modelling, 165: 107–126.CrossRefGoogle Scholar
  10. Miller, E.J., Hunt, J.D., Abraham J.E. and Salvini P.A. (2004) Microsimulating urban systems, Computers, Environment and Urban Systems, 28: 9–44.CrossRefGoogle Scholar
  11. Timmermans, H.J.P. (2003) The saga of integrated land use-transport modeling: how many more dreams before we wake up?Paper presented at the 10th International Conference on Travel Behaviour Research, Lucerne, 10–15 August .Google Scholar
  12. Van Wissen, L. (2000) A micro-simulation model of firms: applications of concepts of the demography of the firm, Papers in Regional Science, 79: 111–134.CrossRefGoogle Scholar
  13. Veldhuisen, K.J., Timmermans, H.J.P. and Kapoen, L.L. (2005) Simulating the effects of urban development on activity-travel patterns: an application of Ramblas to the Randstad North Wing, Environment and Planning B, 32(4): 567–580.CrossRefGoogle Scholar
  14. Waddell, P., Borning, A., Noth, M. Freier, N., Becke M. and Ulfarsson, G. (2003) Microsimulation of urban development and location choices: design and implementation of UrbanSim, Networks and Spatial Economics, 3: 43–67.CrossRefGoogle Scholar
  15. Wegener, M., Mackett, R.L. and Simmonds, D.C. (1991) One city, three models: comparison of land-use/transport policy simulation models for Dortmund, Transportation Reviews, 11: 107–129.CrossRefGoogle Scholar

Copyright information

© Springer 2007

Authors and Affiliations

  • D. Ettema
    • 1
  • K. de Jong
    • 1
  • H. Timmermans
    • 2
  • A. Bakema
    • 3
  1. 1.Faculty of GeosciencesUtrecht UniversityThe Netherlands
  2. 2.Urban Planning Group/EIRASSEindhoven University of TechnologyThe Netherlands
  3. 3.Netherlands Environmental Assessment Agency (MNP)Bilthoven

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