Journal article 449 views 41 downloads
Forecasting the crowd: An effective and efficient neural network for citywide crowd information prediction at a fine spatio-temporal scale
Xucai Zhang,
Yeran Sun 
,
Fangli Guan,
Kai Chen,
Frank Witlox,
Haosheng Huang
,
Fangli Guan,
Kai Chen,
Frank Witlox,
Haosheng Huang
Transportation Research Part C: Emerging Technologies, Volume: 143, Start page: 103854
Swansea University Author:
Yeran Sun
-
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DOI (Published version): 10.1016/j.trc.2022.103854
Abstract
Forecasting the crowd: An effective and efficient neural network for citywide crowd information prediction at a fine spatio-temporal scale
| Published in: | Transportation Research Part C: Emerging Technologies |
|---|---|
| ISSN: | 0968-090X |
| Published: |
Elsevier BV
2022
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa60967 |
| Tags: |
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| Keywords: |
Crowd Information, Convolutional Neural Network; k-Nearest Neighbor; Gated Recurrent Unit; Training Time Cost |
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| College: |
Faculty of Science and Engineering |
| Funders: |
Xucai Zhang and Fangli Guan are supported by CSC (China Scholarship Council) [202106380062, 202006270082]. |
| Start Page: |
103854 |