Testing Demand Responsive Shared Transport Services via Agent-Based Simulations

  • Giuseppe InturriEmail author
  • Nadia Giuffrida
  • Matteo Ignaccolo
  • Michela Le Pira
  • Alessandro Pluchino
  • Andrea Rapisarda
Part of the AIRO Springer Series book series (AIROSS, volume 1)


In this paper, an agent-based model is presented to test the feasibility of different configurations of Demand Responsive Shared Transport (DRST) services in a real context. DRST services provide “just-in-time” mobility solutions by dynamically assigning a fleet of vehicles to passenger booking requests taking advantages of Information and Communication Technologies. First results show the impact of different route choice strategies on the system performance and can be useful to help the planning and designing of such services.


Shared mobility Flexible transit Dynamic ride sharing Mobility on demand Agent-based model 


  1. 1.
    Ambrosino, G., Nelson, J.D., Romanazzo, M.: Demand responsive transport services: towards the flexible mobility agency. ENEA. Italian National Agency for New Technologies, Energy and the Environment. ISBN 88-8286-043-4 (2003)Google Scholar
  2. 2.
    Stein, D.M.: Scheduling dial-a-ride transportation systems. Transp. Sci. 12(3), 232–249 (1978)CrossRefGoogle Scholar
  3. 3.
    Bodin, L., Golden, B.: Classication in vehicle routing and scheduling. Networks 11(2), 97–108 (1981)CrossRefGoogle Scholar
  4. 4.
    Shinoda, K., Noda, I., Ohta, M., Kumada, Y., Nakashima, H.: Is dial-a-ride bus reasonable in large scale towns? Evaluation of usability of dial-a-ride systems by simulation. In: Multiagent for Mass User Support, pp. 105–119. Springer (2004)Google Scholar
  5. 5.
    Diana, M.: The importance of information flows temporal attributes for the efficient scheduling of dynamic demand responsive transport services. J. Adv. Transp. 40(1), 23–46 (2006)MathSciNetCrossRefGoogle Scholar
  6. 6.
    D’Orey, P.M., Fernandes, R., Ferreira, M.: Empirical evaluation of a dynamic and distributed taxi-sharing system. In: Proceedings of ITSC 2012, pp. 140–146. IEEE (2012)Google Scholar
  7. 7.
    Quadrifoglio, L., Dessouky, M., Ordonez, F.: A simulation study of demand responsive transit system design. Transp. Res. Part A 42, 718–737 (2008)Google Scholar
  8. 8.
    Le Pira, M., Inturri, G., Ignaccolo, M., Pluchino, A.: Dealing with the complexity of stakeholder interaction in participatory transport planning. In: Zak, J., Hadas, Y., Rossi, R. (eds.) Advanced Concepts, Methodologies and Technologies for Transportation and Logistics; Advances in Intelligent Systems and Computing, vol. 572. Springer International Publishing. (2018)Google Scholar
  9. 9.
    Marcucci, E., Le Pira, M., Gatta, V., Ignaccolo, M., Inturri, G., Pluchino, A.: Simulating participatory urban freight transport policy-making: accounting for heterogeneous stakeholders’ preferences and interaction effects. Transp. Res. Part E 103, 69–86 (2017)CrossRefGoogle Scholar
  10. 10.
    Cheng, S.F., Nguyen, T.D.: Taxisim: a multiagent simulation platform for evaluating taxi fleet operations. In: Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, vol. 02, pp. 14–21 (2011)Google Scholar
  11. 11.
    Lopes, M.M., Martinez, L.M., de Almeida Correia, G.H.: Simulating carsharing operations through agent-based modelling: an application to the city of Lisbon, Portugal. Transp. Res. Procedia 3, (2014)CrossRefGoogle Scholar
  12. 12.
    Čertický, M., Jakob, M., Píbil, R.: Simulation testbed for autonomic demand-responsive mobility systems. In: Autonomic Road Transport Support Systems, pp. 147–164 (2016)CrossRefGoogle Scholar
  13. 13.
    Bonabeau, E.: Agent-based modeling: methods and techniques for simulating human systems. PNAS 99(Suppl 3), 7280–7287 (2002)CrossRefGoogle Scholar
  14. 14.
    Wilensky, U.: NetLogo. Center for Connected Learning and Computer Based Modeling. Northwestern University, Evanston, IL (1999). In:
  15. 15.
    de Dios Ortuzar, J., Willumsen, L.G.: Modelling Transport. Wiley (2011)Google Scholar
  16. 16.
    Pluchino, A., Rapisarda, A., Garofalo, C.: The Peter principle revisited: a computational study. Phys. A 389(3), 467–472 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Giuseppe Inturri
    • 1
    Email author
  • Nadia Giuffrida
    • 2
  • Matteo Ignaccolo
    • 2
  • Michela Le Pira
    • 2
  • Alessandro Pluchino
    • 3
    • 4
  • Andrea Rapisarda
    • 3
    • 4
    • 5
  1. 1.Department of Electric, Electronic and Computer EngineeringUniversity of CataniaCataniaItaly
  2. 2.Department of Civil Engineering and ArchitectureUniversity of CataniaCataniaItaly
  3. 3.Department of Physics and AstronomyUniversity of CataniaCataniaItaly
  4. 4.Infn sezione di CataniaCataniaItaly
  5. 5.Complexity Science Hub ViennaWienAustria

Personalised recommendations