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Car-Specific Metro Train Crowding Prediction Based on Real-Time Load Data
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering.ORCID iD: 0000-0002-4106-3126
2018 (English)In: 2018 21ST INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), IEEE , 2018, p. 78-83Conference paper, Published paper (Refereed)
Abstract [en]

The paper formulates the car-specific metro train crowding prediction problem based on real-time load data and evaluates the performance of several prediction methods (stepwise regression, lasso, and boosted tree ensembles). The problem is studied for multiple stations along a metro line in Stockholm, Sweden. Prediction accuracy is evaluated with respect to absolute passenger loads and predefined discrete crowding levels. When available, predictions with real-time load data significantly outperform historical averages, with accuracy improvements varying in magnitude across target stations and prediction horizons.

Place, publisher, year, edition, pages
IEEE , 2018. p. 78-83
Series
IEEE International Conference on Intelligent Transportation Systems-ITSC, ISSN 2153-0009
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:kth:diva-244589ISI: 000457881300013Scopus ID: 2-s2.0-85060472544ISBN: 978-1-7281-0323-5 (print)OAI: oai:DiVA.org:kth-244589DiVA, id: diva2:1291336
Conference
21st IEEE International Conference on Intelligent Transportation Systems (ITSC), NOV 04-07, 2018, Maui, HI
Note

QC 20190225

Available from: 2019-02-25 Created: 2019-02-25 Last updated: 2019-08-20Bibliographically approved

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Jenelius, Erik

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  • apa
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  • ieee
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  • vancouver
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Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • text
  • asciidoc
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