Dynamic journeying under uncertainty

Research output: Scientific - peer-reviewArticle

Details

Original languageEnglish
Pages (from-to)455-471
Number of pages17
JournalEuropean Journal of Operational Research
Volume225
Issue number3
StatePublished - 16 Mar 2013
MoE publication typeA1 Journal article-refereed

Researchers

Research units

Abstract

We introduce a journey planning problem in multi-modal transportation networks under uncertainty. The goal is to find a journey, possibly involving transfers between different transport services, from a given origin to a given destination within a specified time horizon. Due to uncertainty in travel times, the arrival times of transport services at public transport stops are modeled as random variables. If a transfer between two services is rendered unsuccessful, the commuter has to reconsider the remaining path to the destination. The problem is modeled as a Markov decision process in which states are defined as paths in the transport network. The main contribution is a backward induction method that generates an optimal policy for traversing the public transport network in terms of maximizing the probability of reaching the destination in time. By assuming history independence and independence of successful transfers between services we obtain approximate methods for the same problem. Analysis and numerical experiments suggest that while solving the path dependent model requires the enumeration of all paths from the origin to the destination, the proposed approximations may be useful for practical purposes due to their computational simplicity. In addition to on-time arrival probability, we show how travel and overdue costs can be taken into account, making the model applicable to freight transportation problems. © 2012 Elsevier B.V. All rights reserved.

    Research areas

  • Itinerary planning problem, Markov decision processes, Stochastic processes, Stochastic shortest path problem, Transportation

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