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K shortest paths in stochastic time-dependent networks

Author

Listed:
  • Nielsen, Lars Relund

    (Biometry Research Unit)

  • Pretolani, Daniele

    (Dipartimento de Matematica e Informatica)

  • Andersen, Kim Allan

    (Department of Business Studies)

Abstract

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
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    References listed on IDEAS

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    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.
    2. Miller-Hooks, Elise & Mahmassani, Hani, 2003. "Path comparisons for a priori and time-adaptive decisions in stochastic, time-varying networks," European Journal of Operational Research, Elsevier, vol. 146(1), pages 67-82, April.
    3. Nielsen, Lars Relund & Pretolani, Daniele & Andersen, Kim Allan, 2004. "Finding the K shortest hyperpaths using reoptimization," CORAL Working Papers L-2004-04, University of Aarhus, Aarhus School of Business, Department of Business Studies.
    4. Elise D. Miller-Hooks & Hani S. Mahmassani, 2000. "Least Expected Time Paths in Stochastic, Time-Varying Transportation Networks," Transportation Science, INFORMS, vol. 34(2), pages 198-215, May.
    5. Randolph W. Hall, 1986. "The Fastest Path through a Network with Random Time-Dependent Travel Times," Transportation Science, INFORMS, vol. 20(3), pages 182-188, August.
    6. Amir Eiger & Pitu B. Mirchandani & Hossein Soroush, 1985. "Path Preferences and Optimal Paths in Probabilistic Networks," Transportation Science, INFORMS, vol. 19(1), pages 75-84, February.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Kjeldsen, Karina Hjortshøj, 2008. "Classification of routing and scheduling problems in liner shipping," CORAL Working Papers L-2008-06, University of Aarhus, Aarhus School of Business, Department of Business Studies.
    2. 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.
    3. 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.
    4. Tarun Rambha & Stephen D. Boyles & S. Travis Waller, 2016. "Adaptive Transit Routing in Stochastic Time-Dependent Networks," Transportation Science, INFORMS, vol. 50(3), pages 1043-1059, August.
    5. Mohammad Hossein Keyhani & Mathias Schnee & Karsten Weihe, 2017. "Arrive in Time by Train with High Probability," Transportation Science, INFORMS, vol. 51(4), pages 1122-1137, November.

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