A classification of public transit users with smart card data based on time series distance metrics and a hierarchical clustering method (English)
- New search for: He, Li
- New search for: Agard, Bruno
- New search for: Trépanier, Martin
- New search for: He, Li
- New search for: Agard, Bruno
- New search for: Trépanier, Martin
In:
Transportmetrica A: Transport Science
;
16
, 1
;
56-75
;
2020
- Article (Journal) / Electronic Resource
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Title:A classification of public transit users with smart card data based on time series distance metrics and a hierarchical clustering method
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Contributors:
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Published in:Transportmetrica A: Transport Science ; 16, 1 ; 56-75
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Publisher:
- New search for: Taylor & Francis
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Publication date:2020-12-20
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Size:20 pages
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ISSN:
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DOI:
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Type of media:Article (Journal)
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Type of material:Electronic Resource
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Language:English
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Keywords:
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Source:
Table of contents – Volume 16, Issue 1
The tables of contents are generated automatically and are based on the data records of the individual contributions available in the index of the TIB portal. The display of the Tables of Contents may therefore be incomplete.
- 1
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Special Issue on Spatiotemporal Big Data Analytics for Transportation ApplicationsChen, Bi Yu / Kwan, Mei-Po et al. | 2020
- 5
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Using spatio-temporal data for estimating missing cycling counts: a multiple imputation approachEl Esawey, Mohamed et al. | 2020
- 23
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Crowding valuation in urban tram and bus transportation based on smart card dataYap, Menno / Cats, Oded / van Arem, Bart et al. | 2020
- 43
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Shared-use mobility competition: a trip-level analysis of taxi, bikeshare, and transit mode choice in Washington, DCWelch, Timothy F. / Gehrke, Steven R. / Widita, Alyas et al. | 2020
- 56
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A classification of public transit users with smart card data based on time series distance metrics and a hierarchical clustering methodHe, Li / Agard, Bruno / Trépanier, Martin et al. | 2020
- 76
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Understanding public transit patterns with open geodemographics to facilitate public transport planningLiu, Yunzhe / Cheng, Tao et al. | 2020
- 104
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Real-time road traffic state prediction based on kernel-KNNXu, Dongwei / Wang, Yongdong / Peng, Peng / Beilun, Shen / Deng, Zhang / Guo, Haifeng et al. | 2020
- 119
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Predicting future locations of moving objects with deep fuzzy-LSTM networksLi, Mingxiao / Lu, Feng / Zhang, Hengcai / Chen, Jie et al. | 2020
- 137
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Unveiling cabdrivers’ dining behavior patterns for site selection of ‘taxi canteen’ using taxi trajectory dataZhao, Pengxiang / Liu, Xintao / Kwan, Mei-Po / Shi, Wenzhong et al. | 2020