Agent-Based Modelling for Urban Planning Current Limitations and Future Trends

  • Pascal PerezEmail author
  • Arnaud Banos
  • Chris Pettit
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10051)


With the global population expected to increase form 7.3 billion in 2015 to 9.5 billion by 2050 [41], smart city planning is becoming increasingly important. This is further exasperated by the fact that an increasing number of people are relocating to cities as we live in a highly urbanised world. Cities are evolving in complex and multi-dimensional ways that can no longer be limited to land use and transport development. In increasingly important that cities planning embraces a more holistic, participatory and iterative approach that balances productivity, livability and sustainability outcomes. A new generation of bottom up, highly granular, highly dynamic and spatially explicit models have emerged to support evidence-based and adaptive urban planning. Agent-based modelling, in particular, has emerged as a dominant paradigm to create massive simulations backed by ever-increasing computing power. In this paper we point at current limitations of pure bottom-up approaches to urban modelling and argue for more flexible frameworks mixing other modelling paradigms, particularly participatory planning approaches. Then, we explore four modelling challenges and propose future trends for agent-based modelling of urban systems to better support planning decisions.


Agent-based modelling Key challenges Urban modelling Urban planning 


  1. 1.
    Arbesman, S.: Overcomplicated: Technology at the Limits of Comprehension. Current, New-York (2016). 244 pGoogle Scholar
  2. 2.
    Arthur, B.: Urban systems and historical path dependence. In: Ausubel, J., Herman, R. (eds.) Cities and Their Vital Systems: Infrastructure Past, Present and Future, pp. 85–97. National Academy of Engineering, Washington DC (1988)Google Scholar
  3. 3.
    Axelrod, R.: The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton Studies in Complexity. Princeton University Press, NJ (1997). 232 pGoogle Scholar
  4. 4.
    Balmer, M., Axhausen, K., Nagel, K.: Agent-based demand-modeling framework for large-scale microsimulations. Transp. Res. Rec. J. Transp. Res. Board 1985, 125–134 (2004)CrossRefGoogle Scholar
  5. 5.
    Batty, M.: A chronicle of scientific planning: the Anglo-American modeling experience. J. Am. Plann. Assoc. 60(1), 7–16 (1994)CrossRefGoogle Scholar
  6. 6.
    Batty, M.: Fifty Years of urban modeling: macro-statics to micro-dynamics. In: Albeverio, S., Andrey, D., Giordano, P., Vancheri, A. (eds.) The Dynamics of Complex Urban Systems An Interdisciplinary Approach, pp. 1–20. Physica-Verlag, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Batty, M.: The New Science of Cities. MIT Press, US (2013)Google Scholar
  8. 8.
    Batty, M.: Can it happen again? planning support, Lee’s requiem and the rise of the smart cities movement. Environ. Plann. B Plann. Des. 41, 388–391 (2014)CrossRefGoogle Scholar
  9. 9.
    Becu, N., Frascaria-Lacoste, N., Latune, J.: Experiential learning based on the newdistrict asymmetric simulation game: results of a dozen gameplay sessions. In: Hybrid Simulation and Gaming in the Networked Society: The 46th ISAGA Annual Conference 2015 (2016). www.hal-01253024
  10. 10.
    Bretagnolle, A., Daude, E., Pumain, D.: From theory to modelling: urban systems as complex systems. Cybergeo Eur. J. Geogr. (2006). Accessed 19 Sep 16
  11. 11.
    Brommelstroet, M., Pelzer, P.: Forty years after Lee’s requiem: are we beyond the seven sins? Environ. Plann. B Plann. Des. 41, 381–391 (2014)CrossRefGoogle Scholar
  12. 12.
    Casas, J., Perarnau, J., Torday, A.: The need to combine different traffic modelling levels for effectively tackling large scale projects adding a hybrid meso/micro approach. Procedia Soc. Behav. Sci. 20, 251–262 (2011)CrossRefGoogle Scholar
  13. 13.
    Cottineau, C., Chapron, P., Reuillon, R.: Growing models from the bottom up. An evaluation-based incremental modelling method (EBIMM) applied to the simulation of systems of cities. J. Artif. Soc. Soc. Simul. 18(4), 9 (2015)Google Scholar
  14. 14.
    Couclelis, H.: Modelling frameworks, paradigms, and approaches. In: Clarke, K.C., Parks, B.E., Crane, M.P. (eds.) Geographic Information Systems and Environmental Modelling. Prentice Hall, London (2002)Google Scholar
  15. 15.
    Couch, C.: Urban Planning: An Introduction. Palgrave Macmillan, London (2016). 344 pCrossRefGoogle Scholar
  16. 16.
    Crooks, A., Castle, C., Batty, M.: Key challenges in agent-based modelling for geo-spatial simulation. Comput. Environ. Urban Syst. 32(6), 417–430 (2008)CrossRefGoogle Scholar
  17. 17.
    Dray, A., Perez, P., Jones, N., Le Page, C., D’Aquino, P., White, I., Auatabu, T.: The AtollGame experience: from knowledge engineering to a computer-assisted role playing game. J. Artif. Soc. Soc. Simul. 9, 1 (2006)Google Scholar
  18. 18.
    Epstein, J.M.: Generative Social Science: Studies in Agent-Based Computational Modeling. Princeton University Press, Princeton (2007). 350 pGoogle Scholar
  19. 19.
    Fosset, P., Banos, A., Beck, E., Chardonnel, S., Lang, C., Marilleau, N., Thévenin, T.: Exploring intra-urban accessibility and impacts of pollution policies with an agent-based simulation platform: GaMiroD. Systems 4, 5 (2016)CrossRefGoogle Scholar
  20. 20.
    Geroliminis, N., Sun, J.: Properties of a well-defined macroscopic fundamental diagram for urban traffic. Transp. Res. Part B 45, 605–617 (2011)CrossRefGoogle Scholar
  21. 21.
    Harris, B.: The real issues concerning Lee’s requiem. J. Am. Plann. Assoc. 60(1), 31–34 (1994)CrossRefGoogle Scholar
  22. 22.
    Huynh, N., Perez, P., Berryman, M., Barthelemy, J.: Simulating Transport and land use interdependencies for strategic urban planning - an agent based modelling approach. Systems 3(4), 177–210 (2015)CrossRefGoogle Scholar
  23. 23.
    Klosterman, R.E.: The what if? collaborative planning support system. Environ. Plann. B Plann. Des. 26(3), 393–408 (1999)CrossRefGoogle Scholar
  24. 24.
    Klosterman, R.E., Pettit, C.J.: An update on planning support systems. Environ. Plann. B Plann. Des. 32(4), 477–484 (2005)CrossRefGoogle Scholar
  25. 25.
    Lee Jr., D.B.: Requiem for large-scale models. J. Am. Inst. Plann. 39(3), 163–178 (1973)CrossRefGoogle Scholar
  26. 26.
    Lee Jr., D.B.: Retrospective on large-scale urban models. J. Am. Plann. Assoc. 60(1), 35–40 (1994)CrossRefGoogle Scholar
  27. 27.
    Macal, C.M., North, M.J.: Tutorial on agent-based modelling and simulation. J. Simul. 4(3), 151–162 (2010)CrossRefGoogle Scholar
  28. 28.
    Melbourne-Thomas, J., Johnson, C.R., Perez, P., Eustache, J., Fulton, E.A., Cleland, D.: Coupling biophysical and socioeconomic models for coral reef systems in Quintana Roo, Mexican Caribbean. Ecol. Soc. 16(3), 23 (2011)Google Scholar
  29. 29.
    Perez, P.: Science to inform and Models to engage. In: Finnigan, J., Raupack, M. (eds.) Negotiating Our Future: Living scenarios for Australia to 2050, vol. 2, pp. 147–160. Australian Academy of Science, Canberra (2013)Google Scholar
  30. 30.
    Pettit, C.J., Klosterman, R.E., Delaney, P., Whitehead, A.L., Kujala, H., Bromage, A., NinoRuiz, M.: The online what if? planning support system: a land suitability application in Western Australia. Appl. Spat. Anal. Policy 8(2), 93–112 (2015)CrossRefGoogle Scholar
  31. 31.
    Pettit, C.: Use of a collaborative GIS-based planning support system to assist in formulating a sustainable development scenario for Hervey Bay, Australia. Environ. Plann. B Plann. Des. 32(4), 523–545 (2005)CrossRefGoogle Scholar
  32. 32.
    Pettit, C., Pullar, D.: A way forward for land use planning to achieve policy goals using spatial modeling scenarios. Environ. Plann. B Plann. Des. 31, 213–233 (2004)CrossRefGoogle Scholar
  33. 33.
    Pumain, D., Sanders, L., Bretagnolle, A., Glisse, B., Mathian, H.: The future of urban systems. In: Lane, D., Pumain, D., Van der Leeuw, S., West, G. (eds.) Complexity perspectives on innovation and social change ISCOM. Methods Series, pp. 331–359. Springer, Berlin (2009)CrossRefGoogle Scholar
  34. 34.
    Pumain, D., Sanders, L.: Theoretical principles in interurban simulation models: a comparison. Environ. Plann. A 45, 2243–2260 (2013)CrossRefGoogle Scholar
  35. 35.
    Rasouli, S., Timmermans, H.: Uncertainty in predicted sequences of activity travel episodes: measurement and analysis. Transp. Res. Rec. J. Transp. Res. Board 2382, 46–53 (2013)CrossRefGoogle Scholar
  36. 36.
    Sanders, L., Pumain, D., Mathian, H., Guerin-Pace, F., Bura, S.: SIMPOP: a multi-agent system for the study of urbanism. Environ. Plann. B 24, 287–305 (1997)CrossRefGoogle Scholar
  37. 37.
    Simon, H.: The architecture of complexity. Proc. Am. Philos. Soc. 106, 467–482 (1962)Google Scholar
  38. 38.
    Sole, R., Manrubia, S.C., Luque, B., Delgado, J., Bascompte, J.: Phase transitions and complex systems: simple, non-linear models capture complex systems at the edge of chaos. Complexity 1(4), 13–25 (1996)CrossRefGoogle Scholar
  39. 39.
    Taplin, J., Taylor, M., Biermann, S.: Transport Modelling Review: Independent Review. Planning and Transport Research Centre (PATREC), Curtin University, Perth (2014). 124 pGoogle Scholar
  40. 40.
    Trubka, R., Glackin, S., Lade, O., Pettit, C.J.: A web-based 3D visualisation and assessment system for urban precinct scenario modelling. ISPRS J. Photogrammetry Remote Sens. 117, 175–186 (2016)CrossRefGoogle Scholar
  41. 41.
    United Nations. Population Division. World Population Prospects: The 2015 Revision: Highlights. UN (2015)Google Scholar
  42. 42.
    Waddell, P.: UrbanSim: modeling urban development for land use, transportation, and environmental planning. J. Am. Plann. Assoc. 68(3), 297–314 (2002)CrossRefGoogle Scholar
  43. 43.
    Ward, J.A., Evans, A.J., Malleson, N.S.: Dynamic calibration of agent based models using data assimilation. R. Soc. Open Sci. 3(4), 150703 (2014)MathSciNetCrossRefGoogle Scholar
  44. 44.
    Wegener, M.: The future of mobility in cities: challenges for urban modelling. Transp. Policy 29, 275–282 (2013)CrossRefGoogle Scholar
  45. 45.
    Zeigler, B.P.: Discrete event system specification framework for self-improving healthcare service systems. IEEE Syst. J. 99, 1–12 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.SMART Infrastructure FacilityUniversity of WollongongWollongongAustralia
  2. 2.UMR Géographie-cités, CNRSParisFrance
  3. 3.City Futures Research CentreUniversity of New South WalesKensingtonAustralia

Personalised recommendations