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Measurement of Service Quality of a Public Transport System, Through Agent-based Simulation Software

  • Mauro Callejas-CuervoEmail author
  • Helver A. Valero-Bustos
  • Andrea C. Alarcón-Aldana
  • Miroslava Mikušova
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 830)

Abstract

An agent-based modeling software is here presented which simulates the measurement of the quality of the service offered by a collective public transport system, through the evaluation of the variables of comfort and speed. The simulator takes into account the trajectory of a route from the bus terminal to its last stop, pausing at each of the stops in the bus itinerary. The software allows for the configuration of the location of each stop, the speed per segment, the distribution of the generation and attraction of tickets per stop, among others. The output information shows the number of passengers waiting, those who leave, journey time, distance covered, and passengers served. In the trajectory tested, an average of 3.9 was obtained with regard to comfort and a 3.1 with regard to speed, using a scale of 1–5.

Keywords

Service quality Collective public transport Agent based simulation software Comfort Speed 

References

  1. 1.
    Davidsson, P., Klügl, F., Verhagen, H.: Simulation of complex systems. In: Magnani, L., Bertolotti, T. (eds.) Springer Handbook of Model-Based Science. Springer Handbooks, pp. 783–797. Springer, Cham (2017)CrossRefGoogle Scholar
  2. 2.
    Adelt, F., Weyer, J., Hoffmann, S., Ihrig, A.: Simulation of the governance of complex systems (SimCo): basic concepts and experiments on urban transportation. J. Artif. Soc. Soc. Simul. 21(2), 1–2 (2018)CrossRefGoogle Scholar
  3. 3.
    Kelley, B.N., Williams, R.A., Holland, J.L., Schnarr, O.C., Allen B.D.: A persistent simulation environment for autonomous systems. In: Aviation Technology, Integration, and Operations Conference. Atlanta, Georgia (2018)Google Scholar
  4. 4.
    Carole, A., Patrick, T., Julie, D., Benoit, G.: BDI vs FSM agents in social simulations for raising awareness in disasters: a case study in Melbourne bushfires. Int. J. Inf. Syst. Crisis Response Manag. 9(1), 27–44 (2017)CrossRefGoogle Scholar
  5. 5.
    Bourgais, M., Taillandier, P., Vercouter, L.: Enhancing the behavior of agents in social simulations with emotions and social relations. In: Dimuro, G., Antunes, L. (eds,) Multi-Agent Based Simulation XVIII. MABS 2017. Lecture Notes in Computer Science, vol. 10798 (2018)Google Scholar
  6. 6.
    Mozhgan, K.C., Paul, R.: Simulating heterogeneous behaviors in complex systems on GPUs. Simul. Model. Pract. Theory 83, 3–17 (2018)CrossRefGoogle Scholar
  7. 7.
    Hulme, A., Thompson, J., Nielsen, R.O., Read, G.J.M., Salmon, P.M.: Towards a complex systems approach in sports injury research: simulating running-related injury development with agent-based modelling. Br. J. Sports Med. 1–11 (2018)Google Scholar
  8. 8.
    Wu, N., Wenbo, L., Hailei, W., Chengxin, X., Dan, G.: Capacity-constrained contraflow adaption for lane reconfiguration in evacuation planning. J. Adv. Transp. 14pp. (2018)Google Scholar
  9. 9.
    Eric, N.B., Charles, A.A., Kwame, K.O.: Volume warrants for major and minor roads left turning traffic lanes at unsignalized T-intersections: a case study using VISSIM modelling. J. Traffic Transp. Eng. 5(5), 417–428 (2018)Google Scholar
  10. 10.
    Novikov, A., Zyryanov, V., Feofilova, A.: Dynamic traffic re-routing as a method of reducing the congestion level of road network elements. J. Appl. Eng. Sci. 16(1), 70–74 (2018)CrossRefGoogle Scholar
  11. 11.
    Nigel, G., Troitzsch, K.G.: Simulation for the Social Scientist, 1\(^{a}\) edn, pp. 67–69. Open University Press, New York (2005)Google Scholar
  12. 12.
    Valentino, C., Aram, G., Kristina, L.: Top-down vs bottom-up methodologies in multi-agent system design. Auton. Robot. 24(3), 303–313 (2008)CrossRefGoogle Scholar
  13. 13.
    Rumbaugh, J., Jacobson, I., Booch, G.: The Unified Modeling Language Reference Manual, 1st edn. Addison-Wesley Professional, Massachusetts (1999)Google Scholar
  14. 14.
    Fikar, C., Hirsch, P., Nolz, P.C.: Cent. Eur. J. Oper. Res. 26(2), 423–442 (2018)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Universidad Pedagógica y Tecnológica de ColombiaTunjaColombia
  2. 2.University of ZilinaŽilinaSlovakia

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