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Authors: Yan Hong and Du Xiaoping

Affiliation: Software College of Beihang University, China

Keyword(s): Urban Railway Traffic, Route Selection, Traveler Classification.

Abstract: With the rapid development of urban rail transit network, traveler’s route decision become more difficult to make and travelers’ route preferences vary with their characteristics. This study proposed a route recommendation algorithm with the least generalized travel cost based on the classification of traveler’s personal characteristic. The generalized travel cost model was established with the consideration of LOS variables (e.g. in-vehicle time, transfer time, number of transfers, in-vehicle traveler density, etc) and then a traveler classifier was constructed based on the K- nearest neighbor algorithm by machine learning how travelers’ characteristics affect their route choice intentions, thus the optimal route with the least generalized cost for each type of travelers being generated. Finally, the model and algorithm were verified to be valid with the data from Beijing subway network.

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Paper citation in several formats:
Hong, Y. and Xiaoping, D. (2015). Route Recommendation Algorithm for Railway Transit Travelers based on Classification of Personal Characteristics. In Proceedings of the Information Science and Management Engineering III - ISME; ISBN 978-989-758-163-2, SciTePress, pages 120-125. DOI: 10.5220/0006020201200125

@conference{isme15,
author={Yan Hong. and Du Xiaoping.},
title={Route Recommendation Algorithm for Railway Transit Travelers based on Classification of Personal Characteristics},
booktitle={Proceedings of the Information Science and Management Engineering III - ISME},
year={2015},
pages={120-125},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006020201200125},
isbn={978-989-758-163-2},
}

TY - CONF

JO - Proceedings of the Information Science and Management Engineering III - ISME
TI - Route Recommendation Algorithm for Railway Transit Travelers based on Classification of Personal Characteristics
SN - 978-989-758-163-2
AU - Hong, Y.
AU - Xiaoping, D.
PY - 2015
SP - 120
EP - 125
DO - 10.5220/0006020201200125
PB - SciTePress

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