IDEAS home Printed from
   My bibliography  Save this paper

K shortest paths in stochastic time-dependent networks


  • Nielsen, Lars Relund

    (Biometry Research Unit)

  • Pretolani, Daniele

    (Dipartimento de Matematica e Informatica)

  • Andersen, Kim Allan

    () (Department of Business Studies)


A substantial amount of research has been devoted to the shortest path problem in networks where travel times are stochastic or (deterministic and) time-dependent. More recently, a growing interest has been attracted by networks that are both stochastic and time-dependent. In these networks, the best route choice is not necessarily a path, but rather a time-adaptive strategy that assigns successors to nodes as a function of time. In some particular cases, the shortest origin-destination path must nevertheless be chosen a priori, since time-adaptive choices are not allowed. Unfortunately, finding the a priori shortest path is NP-hard, while the best time-adaptive strategy can be found in polynomial time. In this paper, we propose a solution method for the a priori shortest path problem, and we show that it can be easily adapted to the ranking of the first K shortest paths. Moreover, we present a computational comparison of time-adaptive and a priori route choices, pointing out the effect of travel time and cost distributions. The reported results show that, under realistic distributions, our solution methods are effective

Suggested Citation

  • Nielsen, Lars Relund & Pretolani, Daniele & Andersen, Kim Allan, 2004. "K shortest paths in stochastic time-dependent networks," CORAL Working Papers L-2004-05, University of Aarhus, Aarhus School of Business, Department of Business Studies.
  • Handle: RePEc:hhb:aarbls:2004-005

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Pretolani, Daniele, 2000. "A directed hypergraph model for random time dependent shortest paths," European Journal of Operational Research, Elsevier, vol. 123(2), pages 315-324, June.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Nielsen, Lars Relund & Andersen, Kim Allan & Pretolani, Daniele, 2006. "Bicriterion a priori route choice in stochastic time-dependent networks," CORAL Working Papers L-2006-10, University of Aarhus, Aarhus School of Business, Department of Business Studies.
    2. Nielsen, Lars Relund & Kristensen, Anders Ringgaard, 2006. "Finding the K best policies in a finite-horizon Markov decision process," European Journal of Operational Research, Elsevier, vol. 175(2), pages 1164-1179, December.


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hhb:aarbls:2004-005. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Helle Vinbaek Stenholt). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.