Analysis of the spatiotemporal riding modes of dockless shared bicycles based on tensor decomposition-Reference-Cited by-同舟云学术

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Analysis of the spatiotemporal riding modes of dockless shared bicycles based on tensor decomposition

Published:2020-05-28 Issue:11 Volume:34 Page:2225-2242
ISSN:1365-8816
Container-title:International Journal of Geographical Information Science
language:en
Short-container-title:International Journal of Geographical Information Science

Author:

Cao Min¹²³,Huang Mengxue¹²⁴,Ma Shangjing¹²³,Lü Guonian¹²³,Chen Min¹²³

Affiliation:

1. School of Geography, Nanjing Normal University, Nanjing, China

2. State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing, China

3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China

4. Norinco Group North Information Control Research Academy Croup Co., Ltd, Nanjing, China

Funder

NSF of China

Priority Academic Program Development of Jiangsu Higher Education Institutions

Publisher

Informa UK Limited

Subject

Library and Information Sciences,Geography, Planning and Development,Information Systems

Link

https://www.tandfonline.com/doi/pdf/10.1080/13658816.2020.1768259

Reference56 articles.

1. A deep learning approach on short-term spatiotemporal distribution forecasting of dockless bike-sharing system

2. Exploring Bikesharing Travel Patterns and Trip Purposes Using Smart Card Data and Online Point of Interests

3. Using crowdsourced data to monitor change in spatial patterns of bicycle ridership

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1. Spatio-temporal Analysis of Precipitation Effects on Bicycle-Sharing Systems with Tensor Approach;Journal of Geovisualization and Spatial Analysis;2023-11-10

2. High-dimensional data analytics in civil engineering: A review on matrix and tensor decomposition;Engineering Applications of Artificial Intelligence;2023-10

3. Toward urban traffic scenarios and more: a spatio-temporal analysis empowered low-rank tensor completion method for data imputation;International Journal of Geographical Information Science;2023-07-19

4. Analysis of mobility patterns for urban taxi ridership: the role of the built environment;Transportation;2023-02-22

5. Discovering spatiotemporal flow patterns: where the origin–destination map meets empirical orthogonal function decomposition;Cartography and Geographic Information Science;2023-02-21

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