Simulation Testbed for Autonomic Demand-Responsive Mobility Systems

Chapter
Part of the Autonomic Systems book series (ASYS)

Abstract

In this chapter, we describe an open-source simulation testbed for emerging autonomic mobility systems, in which transport vehicles and other resources are automatically managed to serve a dynamically changing transport demand. The testbed is designed for testing and evaluation of various planning, coordination and resource allocation mechanisms for the control and management of autonomic mobility systems. It supports all stages of the experimentation process, from the implementation of tested control mechanisms and the definition of experiment scenarios through simulation execution up to the analysis and interpretation of the results. The testbed aims to accelerate the development of control mechanisms for autonomic mobility systems and to facilitate their mutual comparison using well-defined benchmark scenarios. We also demonstrate how it can be used to select the most suitable control mechanism for a specific use case and to approximate operational costs and initial investments needed to deploy a specific autonomic mobility system.

Keywords

Algorithms Demand-responsive transport On-demand transport Simulation Testbed 

References

  1. 1.
    Agatz, N., Erera, A.L., Savelsbergh, M.W., Wang, X.: Dynamic ride-sharing: a simulation study in metro Atlanta. Proc. Soc. Behav. Sci. 17, 532–550 (2011). Papers selected for the 19th International Symposium on Transportation and Traffic TheoryGoogle Scholar
  2. 2.
    Attanasio, A., Cordeau, J.-F., Ghiani, G., Laporte, G.: Parallel tabu search heuristics for the dynamic multi-vehicle dial-a-ride problem. Parallel Comput. 30(3), 377–387 (2004)CrossRefGoogle Scholar
  3. 3.
    Bailey Jr., W.A., Clark Jr., T.D.: A simulation analysis of demand and fleet size effects on taxicab service rates. In: Proceedings of the 19th Conference on Winter Simulation, pp. 838–844. ACM, New York (1987)Google Scholar
  4. 4.
    Banks, J., Carson, J.S., Nelson, B.L., Nicol, D.M., et al.: Discrete-Event System Simulation. Pearson Prentice Hall, Upper Saddle River (2005)Google Scholar
  5. 5.
    Bodin, L., Golden, B.: Classification in vehicle routing and scheduling. Networks 11(2), 97–108 (1981)CrossRefGoogle Scholar
  6. 6.
    Čertický, M., Jakob, M., Píbil, R.: Analyzing on-demand mobility services by agent-based simulation. J. Ubiquitous Syst. Pervasive Netw. 6(1), 17–26 (2015)Google Scholar
  7. 7.
    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-Volume 02, pp. 14–21 (2011)Google Scholar
  8. 8.
    de Dios Ortuzar, J., Willumsen, L.G.: Modelling Transport. Wiley, New Jersey (1994)Google Scholar
  9. 9.
    Deflorio, F.P., Dalla Chiara, B., Murro, A., SpA, M.A.: Simulation and performance of DRTS in a realistic environment. In: Proceedings of 9th Meeting EWGT on Intermodality, Sustainability and ITS/13th Mini EURO Conference on Handling Uncertainty in the Analysis of Traffic and Transportation Systems (2002)Google Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    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, Anchorage (2012)Google Scholar
  12. 12.
    Drewe, P.: What about time in urban planning & design in the ICT age. In: Proceedings of the CORP Conference (2005)Google Scholar
  13. 13.
    Edwards, D., Elangovan, A., Watkins, K.: Reaching low-density urban areas with the network-inspired transportation system. In: 15th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2012, pp. 826–831. IEEE, Anchorage (2012)Google Scholar
  14. 14.
    Fu, L.: A simulation model for evaluating advanced dial-a-ride paratransit systems. Transp. Res. A Policy Pract. 36(4), 291–307 (2002)CrossRefGoogle Scholar
  15. 15.
    Geoffrion, A.M.: The purpose of mathematical programming is insight, not numbers. Interfaces 7(1), 81–92 (1976)CrossRefGoogle Scholar
  16. 16.
    Haghani, A., Banihashemi, M.: Heuristic approaches for solving large-scale bus transit vehicle scheduling problem with route time constraints. Transp. Res. A Policy Pract. 36(4), 309–333 (2002)CrossRefGoogle Scholar
  17. 17.
    Hall, R.W.: Discrete models/continuous models. Omega 14(3), 213–220 (1986)CrossRefGoogle Scholar
  18. 18.
    Hauptmeier, D., Krumke, S.O., Rambau, J.: The online dial-a-ride problem under reasonable load. In: Proceedings of the 4th Italian Conference on Algorithms and Complexity, CIAC ’00, pp. 125–136. Springer, London (2000)Google Scholar
  19. 19.
    Horn, M.: Multi-modal and demand-responsive passenger transport systems: a modelling framework with embedded control systems. Transp. Res. A 36(2), 167–188 (2002)MathSciNetGoogle Scholar
  20. 20.
    Jakob, M., Moler, Z.: Modular framework for simulation modelling of interaction-rich transport systems. In: Proceedings of ITSC 2013. IEEE, The Hague (2013)Google Scholar
  21. 21.
    Jlassi, J., Euchi, J., Chabchoub, H.: Dial-a-ride and emergency transportation problems in ambulance services. Comput. Sci. Eng. 2(3), 17–23 (2012)CrossRefGoogle Scholar
  22. 22.
    Kamar, E., Horvitz, E.: Collaboration and shared plans in the open world: studies of ridesharing. In: Proceedings of IJCAI 2009, vol. 9, p. 187 (2009)Google Scholar
  23. 23.
    Kephart, J.O., Chess, D.M.: The vision of autonomic computing. Computer 36(1), 41–50 (2003)CrossRefMathSciNetGoogle Scholar
  24. 24.
    Klügl, F.: Agent-based simulation engineering. PhD thesis, Habilitation Thesis, University of Würzburg (2009)Google Scholar
  25. 25.
    Langevin, A., Mbaraga, P., Campbell, J.F.: Continuous approximation models in freight distribution: an overview. Transp. Res. B Methodol. 30(3), 163–188 (1996)CrossRefGoogle Scholar
  26. 26.
    Lioris, E., Cohen, G., de La Fortelle, A.: Overview of a dynamic evaluation of collective taxi systems providing an optimal performance. In: 2010 IEEE Intelligent Vehicles Symposium (IV), pp. 1110–1115. IEEE, San Diego (2010)Google Scholar
  27. 27.
    Lipmann, M., Lu, X., Paepe, W.E., Sitters, R.A., Stougie, L.: On-line dial-a-ride problems under a restricted information model. Algorithmica 40(4), 319–329 (2004)CrossRefMATHMathSciNetGoogle Scholar
  28. 28.
    Pěchouček, M., Jakob, M., Novák, P.: Towards simulation-aided design of multi-agent systems. In: Programming Multi-Agent Systems, pp. 3–21. Springer, Heidelberg (2012)Google Scholar
  29. 29.
    Quadrifoglio, L., Dessouky, M.: Insertion heuristic for scheduling mobility allowance shuttle transit (MAST) services: sensitivity to service area. In: Computer-Aided Systems in Public Transport. Lecture Notes in Economics and Mathematical Systems, vol. 600. Springer, Heidelberg (2007)Google Scholar
  30. 30.
    Quadrifoglio, L., Dessouky, M.M., Ordóñez, F.: A simulation study of demand responsive transit system design. Transp. Res. A Policy Pract. 42(4), 718–737 (2008)CrossRefGoogle Scholar
  31. 31.
    Regan, A.C., Mahmassani, H.S., Jaillet, P.: Dynamic decision making for commercial fleet operations using real-time information. Transp. Res. Rec. J. Transp. Res. Board 1537(1), 91–97 (1996)CrossRefGoogle Scholar
  32. 32.
    Schuetz, S., Zimmermann, K., Nunzi, G., Schmid, S., Brunner, M.: Autonomic and decentralized management of wireless access networks. IEEE Trans. Netw. Serv. Manag. 4(2), 96–106 (2007)CrossRefGoogle Scholar
  33. 33.
    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: Multi-agent for Mass User Support, pp. 105–119. Springer, Heidelberg (2004)Google Scholar
  34. 34.
    Stein, D.M.: Scheduling dial-a-ride transportation systems. Transp. Sci. 12(3), 232–249 (1978)CrossRefGoogle Scholar
  35. 35.
    Uhrmacher, A.M., Weyns, D.: Multi-Agent Systems: Simulation and Applications. CRC Press, Boca Raton (2010)Google Scholar
  36. 36.
    Wilson, N.H.M., Sussman, J., Goodman, L., Hignnet, B.: Simulation of a computer aided routing system (CARS). In: Proceedings of the Third Conference on Applications of Simulation, pp. 171–183. Winter Simulation Conference (1969)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Agent Technology CenterFaculty of Electrical Engineering, Czech Technical UniversityPragueCzech Republic

Personalised recommendations