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Optimising Routing in an Agent-Centric Synchromodal Network with Shared Information

  • Myrte A. M. De Juncker
  • Frank PhillipsonEmail author
  • Lianne A. M. Bruijns
  • Alex Sangers
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11184)

Abstract

Our research focuses on synchromodal planning problems in which information is shared between all agents in the system and they choose their routes based on an individual optimisation objective. We show the effect of the information availability by developing three different methods to determine the optimal paths, to motivate logistic players to cooperate in a synchromodal system.

Keywords

Synchromodal logistics Agent centric network User equilibrium 

Notes

Acknowledgement

This work has been carried out within the project ‘Complexity Methods for Predictive Synchromodality’ (Comet-PS), supported by NWO (the Netherlands Organisation for Scientific Research), TKI-Dinalog (Top Consortium Knowledge and Innovation) and the Early Research Program ‘Grip on Complexity’ of TNO (The Netherlands Organisation for Applied Scientific Research).

References

  1. 1.
    Akcelik, R.: Travel time functions for transport planning purposes: Davidson’s function, its time dependent form and alternative travel time function. Aust. Road Res. 21(3) (1991)Google Scholar
  2. 2.
    Ben-Akiva, M., Bierlaire, M., Bottom, J., Koutsopoulos, H., Mishalani, R.: Development of a route guidance generation system for real-time application. In: 8th IFAC Symposium on Transportation Systems. No. TRANSP-OR-CONF-2006-063 (1997)Google Scholar
  3. 3.
    Ben-Akiva, M., Bierlaire, M., Koutsopoulos, H., Mishalani, R.: DynaMIT: a simulation-based system for traffic prediction. In: DACCORD Short Term Forecasting Workshop, pp. 1–12 (1998)Google Scholar
  4. 4.
    Birge, J., Ho, J.: Optimal flows in stochastic dynamic networks with congestion. Oper. Res. 41(1), 203–216 (1993)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Carey, M., Subrahmanian, E.: An approach to modelling time-varying flows on congested networks. Transp. Res. Part B: Methodol. 34(3), 157–183 (2000)CrossRefGoogle Scholar
  6. 6.
    Dafermos, S.: Traffic equilibrium and variational inequalities. Transp. Sci. 14(1), 42–54 (1980)MathSciNetCrossRefGoogle Scholar
  7. 7.
    De Juncker, M.: Optimising routing in an agent-centric synchromodal network with shared information. Master’s thesis, Eindhoven University of Technology (2017)Google Scholar
  8. 8.
    Dia, H.: An agent-based approach to modelling driver route choice behaviour under the influence of real-time information. Transp. Res. Part C Emerg. Technol. 10(5), 331–349 (2002)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Friesz, T., Luque, J., Tobin, R., Wie, B.: Dynamic network traffic assignment considered as a continuous time optimal control problem. Oper. Res. 37(6), 893–901 (1989)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Janson, B.: Convergent algorithm for dynamic traffic assignment. Transportation Research Record (1328) (1991)Google Scholar
  11. 11.
    Kaufman, D., Smith, R., Wunderlich, K.: User-equilibrium properties of fixed points in dynamic traffic assignment. Transp. Res. Part C Emerg. Technol. 6(1), 1–16 (1998)CrossRefGoogle Scholar
  12. 12.
    Mahmassani, H., Jayakrishnan, R.: System performance and user response under real-time information in a congested traffic corridor. Transp. Res. Part A Gen. 25(5), 293–307 (1991)CrossRefGoogle Scholar
  13. 13.
    Merchant, D., Nemhauser, G.: A model and an algorithm for the dynamic traffic assignment problems. Transp. Sci. 12(3), 183–199 (1978)CrossRefGoogle Scholar
  14. 14.
    Nagurney, A.: Network Economics: A Variational Inequality Approach, vol. 10. Springer Science & Business Media (2013)Google Scholar
  15. 15.
    Peeta, S., Mahmassani, H.: System optimal and user equilibrium time-dependent traffic assignment in congested networks. Ann. Oper. Res. 60(1), 81–113 (1995)CrossRefGoogle Scholar
  16. 16.
    Peeta, S., Ziliaskopoulos, A.: Foundations of dynamic traffic assignment: The past, the present and the future. Netw. Spat. Econ. 1(3), 233–265 (2001)CrossRefGoogle Scholar
  17. 17.
    Phillipson, F.: A thought on optimisation and self-organisation in synchromodal logistics. Technical report TNO (2017)Google Scholar
  18. 18.
    Ramalingam, G., Reps, T.: An incremental algorithm for a generalization of the shortest-path problem. J. Algorithms 21(2), 267–305 (1996)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Ran, B., Boyce, D., LeBlanc, L.: A new class of instantaneous dynamic user-optimal traffic assignment models. Oper. Res. 41(1), 192–202 (1993)CrossRefGoogle Scholar
  20. 20.
    Ran, B., Shimazaki, T.: A general model and algorithm for the dynamic traffic assignment problems. In: Transport Policy, Management & Technology Towards 2001: Selected Proceedings of the Fifth World Conference on Transport Research, vol. 4 (1989)Google Scholar
  21. 21.
    Tavasszy, L., Behdani, B., Konings, R.: Intermodality and synchromodality. SSRN Online (2015)Google Scholar
  22. 22.
    Ulmer, M.W., Heilig, L., Voß, S.: On the value and challenge of real-time information in dynamic dispatching of service vehicles. Bus. Inf. Syst. Eng. 59(3), 161–171 (2017)CrossRefGoogle Scholar
  23. 23.
    Wardrop, J.: Some theoretical aspects of road traffic research. In: Proceedings of the Institute of Civil Engineers, London (1900)Google Scholar
  24. 24.
    Ziliaskopoulos, A., Mahmassani, H.: Time-dependent, shortest-path algorithm for real-time intelligent vehicle highway system applications. Transportation research record, pp. 94–94 (1993)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Myrte A. M. De Juncker
    • 1
    • 3
  • Frank Phillipson
    • 1
    Email author
  • Lianne A. M. Bruijns
    • 1
    • 2
  • Alex Sangers
    • 1
  1. 1.TNOThe HagueThe Netherlands
  2. 2.Delft University of TechnologyDelftThe Netherlands
  3. 3.Eindhoven University of TechnologyEindhovenThe Netherlands

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