Journal article 455 views 43 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
-
PDF | Accepted Manuscript
©2022 All rights reserved. All article content, except where otherwise noted, is licensed under a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND)
Download (1.75MB)
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
|
| Online Access: |
Check full text
|
| URI: | https://cronfa.swan.ac.uk/Record/cronfa60967 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Keywords: |
Crowd Information, Convolutional Neural Network; k-Nearest Neighbor; Gated Recurrent Unit; Training Time Cost |
|---|---|
| College: |
Faculty of Science and Engineering |
| Funders: |
Xucai Zhang and Fangli Guan are supported by CSC (China Scholarship Council) [202106380062, 202006270082]. |
| Start Page: |
103854 |