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Car-Specific Metro Train Crowding Prediction Based on Real-Time Load Data
KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Byggvetenskap.ORCID-id: 0000-0002-4106-3126
2018 (engelsk)Inngår i: 2018 21ST INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), IEEE , 2018, s. 78-83Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
IEEE , 2018. s. 78-83
Serie
IEEE International Conference on Intelligent Transportation Systems-ITSC, ISSN 2153-0009
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-244589ISI: 000457881300013Scopus ID: 2-s2.0-85060472544ISBN: 978-1-7281-0323-5 (tryckt)OAI: oai:DiVA.org:kth-244589DiVA, id: diva2:1291336
Konferanse
21st IEEE International Conference on Intelligent Transportation Systems (ITSC), NOV 04-07, 2018, Maui, HI
Merknad

QC 20190225

Tilgjengelig fra: 2019-02-25 Laget: 2019-02-25 Sist oppdatert: 2019-08-20bibliografisk kontrollert

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