The Impact of New Mobility Modes on a City: A Generic Approach Using ABM

  • Arnaud GrignardEmail author
  • Luis Alonso
  • Patrick Taillandier
  • Benoit Gaudou
  • Tri Nguyen-Huu
  • Wolfgang Gruel
  • Kent Larson
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)


Mobility is a key issue for city planners. Being able to evaluate the impact of its evolution is complex and involves many factors including new technologies like electric cars, autonomous vehicles and also new social habits like vehicle sharing. We need a better understanding of different scenarios to improve the quality of long-term decisions. Computer simulations can be a tool to better understand this evolution, to discuss different solutions and to communicate the implications of different decisions. In this paper, we propose a new generic model that creates an artificial micro-world which allows the modeler to create and modify new mobility scenarios in a quick and easy way. This not only helps to better understand the impact of new mobility modes on a city, but also fosters a better-informed discussion of different futures. Our model is based on the agent-based paradigm using the GAMA Platform. It takes into account different mobility modes, people profiles, congestion and traffic patterns. In this paper, we review an application of the model of the city of Cambridge.


Agent-based modeling Mobility City Science 


  1. 1.
    Alonso, L., Zhang, Y., Grignard, A., Noyman, A., Sakai, Y., ElKatsha, M., Doorley, R., Larson, K.: Cityscope: a data-driven interactive simulation tool for urban design. Use case volpe. In: ICCS 2018 (2018, to be Published)Google Scholar
  2. 2.
    Alonso-Mora, J., Samaranayake, S., Wallar, A., Frazzoli, E., Rus, D.: On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment. Proc. Natl. Acad. Sci. 114, 462–467 (2017)CrossRefGoogle Scholar
  3. 3.
    Balmer, M., Rieser, M., Meister, K., Charypar, D., Lefebvre, N., Nagel, K., Axhausen, K.: MATSim-T: architecture and simulation times. In: Multi-Agent Systems for Traffic and Transportation Engineering, pp. 57–78 (2009)Google Scholar
  4. 4.
    Czura, G., Taillandier, P., Tranouez, P., Daudé, É.: Mosaiic: city-level agent-based traffic simulation adapted to emergency situations. In: Proceedings of the International Conference on Social Modeling and Simulation, plus Econophysics Colloquium 2014, pp. 265–274. Springer (2015)Google Scholar
  5. 5.
    Fosset, P., Banos, A., Beck, E., Chardonnel, S., Lang, C., Marilleau, N., Piombini, A., Leysens, T., Conesa, A., Andre-Poyaud, I., et al.: Exploring intra-urban accessibility and impacts of pollution policies with an agent-based simulation platform: GaMiroD. Systems 4(1), 5 (2016)CrossRefGoogle Scholar
  6. 6.
    Grignard, A., Taillandier, P., Gaudou, B., Vo, D.A., Huynh, N.Q., Drogoul, A.: GAMA 1.6: advancing the art of complex agent-based modeling and simulation. In: International Conference on Principles and Practice of Multi-Agent Systems, pp. 117–131. Springer (2013)Google Scholar
  7. 7.
    Grimm, V., Berger, U., DeAngelis, D.L., Polhill, J.G., Giske, J., Railsback, S.F.: The ODD protocol: a review and first update. Ecol. Model. 221(23), 2760–2768 (2010)CrossRefGoogle Scholar
  8. 8.
    Gruel, W., Piller, F.: New vision for personal transportation. MIT Sloan Manage. Rev. 57(2), 20–24 (2016)Google Scholar
  9. 9.
    Gruel, W., Stanford, J.M.: Assessing the long-term effects of autonomous vehicles: a speculative approach. Transp. Res. Procedia 13, 18–29 (2016)., towards future innovative transport: visions, trends and methods 43rd European Transport Conference Selected ProceedingsCrossRefGoogle Scholar
  10. 10.
    Horn, M.: Multi-modal and demand-responsive passenger transport systems: a modelling framework with embedded control systems. Transp. Res. Part A Policy Pract. 36, 167–188 (2002)CrossRefGoogle Scholar
  11. 11.
    Krajzewicz, D., Erdmann, J., Behrisch, M., Bieker, L.: Recent development and applications of SUMO - Simulation of Urban MObility. Int. J. Adv. Syst. Meas. 5(3&4), 128–138 (2012)Google Scholar
  12. 12.
    Pavone, M., Smith, S.L., Frazzoli, E., Rus, D.: Robotic load balancing for mobility-on-demand systems. Int. J. Robot. Res. 31(7), 839–854 (2012)CrossRefGoogle Scholar
  13. 13.
    Shepherd, S.: A review of system dynamics models applied in transportation. Transp. B Transp. Dyn. 2(2), 83–105 (2014)MathSciNetGoogle Scholar
  14. 14.
    Taillandier, P.: Traffic simulation with the gama platform. In: International Workshop on Agents in Traffic and Transportation, pp. 8–p (2014)Google Scholar
  15. 15.
    Tranouez, P., Daudé, E., Langlois, P.: A multiagent urban traffic simulation. J. Nonlinear Syst. Appl. 3(2), 98–106 (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Arnaud Grignard
    • 1
    Email author
  • Luis Alonso
    • 1
  • Patrick Taillandier
    • 2
  • Benoit Gaudou
    • 3
  • Tri Nguyen-Huu
    • 4
    • 6
  • Wolfgang Gruel
    • 5
  • Kent Larson
    • 1
  1. 1.MIT Media Lab, City ScienceCambridgeUSA
  2. 2.MIAT, University of Toulouse, INRAToulouseFrance
  3. 3.University Toulouse 1 Capitole, UMR 5505 IRIT, CNRSToulouseFrance
  4. 4.UMMISCO, IRDBondyFrance
  5. 5.Institute for Mobility and Digital InnovationStuttgart Media UniversityStuttgartGermany
  6. 6.IXXI, ENS LyonLyonFrance

Personalised recommendations