Dynamic journeying in scheduled networks

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Dynamic journeying in scheduled networks. / Hame, Lauri; Hakula, Harri.

In: IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, Vol. 14, No. 1, 6293897, 2013, p. 360-369.

Research output: Scientific - peer-reviewArticle

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Author

Hame, Lauri; Hakula, Harri / Dynamic journeying in scheduled networks.

In: IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, Vol. 14, No. 1, 6293897, 2013, p. 360-369.

Research output: Scientific - peer-reviewArticle

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@article{245eeb61549642ca99809e85f4b96433,
title = "Dynamic journeying in scheduled networks",
keywords = "Itinerary planning, Markov decision process, multimodal transportation network",
author = "Lauri Hame and Harri Hakula",
year = "2013",
doi = "10.1109/TITS.2012.2213817",
volume = "14",
pages = "360--369",
journal = "IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS",
issn = "1524-9050",
number = "1",

}

RIS - Download

TY - JOUR

T1 - Dynamic journeying in scheduled networks

AU - Hame,Lauri

AU - Hakula,Harri

PY - 2013

Y1 - 2013

N2 - We study a dynamic-journey planning problem for multimodal transportation networks. The goal is to find a journey, possibly involving transfers between different transport modes, from a given origin to a given destination within a specified time horizon. Transport services are represented as sequences of scheduled legs between nodes in the transportation network. Due to uncertainty in transport services, we assume for each pair of adjacent legs $i$ and $j$ a probability of a successful transfer from $i$ to $j$. If a transfer between two legs is unsuccessful, the customer needs to reconsider the remaining path to the destination. The problem is modeled as a Markov decision process, and the main contribution is a backward induction algorithm that generates an optimal policy for traversing the public transport network in terms of a given objective, e.g., reliability, ride time, waiting time, walking time, or the number of transfers. A straightforward method for maximizing reliability is also suggested, and the algorithms are tested on real-life Helsinki area public transport data. Computational examples show that, with a given input, the proposed algorithms rapidly solve the journeying problem. © 2000-2011 IEEE.

AB - We study a dynamic-journey planning problem for multimodal transportation networks. The goal is to find a journey, possibly involving transfers between different transport modes, from a given origin to a given destination within a specified time horizon. Transport services are represented as sequences of scheduled legs between nodes in the transportation network. Due to uncertainty in transport services, we assume for each pair of adjacent legs $i$ and $j$ a probability of a successful transfer from $i$ to $j$. If a transfer between two legs is unsuccessful, the customer needs to reconsider the remaining path to the destination. The problem is modeled as a Markov decision process, and the main contribution is a backward induction algorithm that generates an optimal policy for traversing the public transport network in terms of a given objective, e.g., reliability, ride time, waiting time, walking time, or the number of transfers. A straightforward method for maximizing reliability is also suggested, and the algorithms are tested on real-life Helsinki area public transport data. Computational examples show that, with a given input, the proposed algorithms rapidly solve the journeying problem. © 2000-2011 IEEE.

KW - Itinerary planning

KW - Markov decision process

KW - multimodal transportation network

UR - http://www.scopus.com/inward/record.url?scp=84879323278&partnerID=8YFLogxK

U2 - 10.1109/TITS.2012.2213817

DO - 10.1109/TITS.2012.2213817

M3 - Article

VL - 14

SP - 360

EP - 369

JO - IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

T2 - IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

JF - IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

SN - 1524-9050

IS - 1

M1 - 6293897

ER -

ID: 12920107