Correlation-based feature selection and parallel spatiotemporal networks for efficient passenger flow forecasting in metro systems-Reference-Cited by-同舟云学术

Correlation-based feature selection and parallel spatiotemporal networks for efficient passenger flow forecasting in metro systems

Published:2024-04-04 Issue: Volume: Page:1-37
ISSN:2324-9935
Container-title:Transportmetrica A: Transport Science
language:en
Short-container-title:Transportmetrica A: Transport Science

Author:

Xiu Cong¹²^ORCID,Zhan Shuguang³^ORCID,Pan Jinyi⁴,Peng Qiyuan¹²,Lin Zhiyuan⁴,Wong S.C.⁵

Affiliation:

1. School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, People’s Republic of China

2. National United Engineering Laboratory of Integrated and Intelligent Transportation, Chengdu, People’s Republic of China

3. School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, People’s Republic of China

4. Institute for Transport Studies, University of Leeds, Leeds, UK

5. Department of Civil Engineering, The University of Hong Kong

Funder

Natural Science Foundation of China

Natural Science Foundation of Sichuan Province

Francis S Y Bong Endowed Professorship in Engineering

Guangdong - Hong Kong - Macau Joint Laboratory Program of the 2020 Guangdong New Innovative Strategic Research Fund, Guangdong Science and Technology Department

National Engineering Laboratory of Integrated Transportation Big Data Application Technology

the High-speed Railway Basic Research Joint Fund of China

National and Local Joint Engineering Laboratory of Comprehensive Intelligent Transportation

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/23249935.2024.2335244

Reference68 articles.

1. Store-and-forward based methods for the signal control problem in large-scale congested urban road networks

2. Highway traffic flow prediction using support vector regression and Bayesian classifier

3. Forecasting histogram time series with k-nearest neighbours methods

4. Bai S. J. Z. Kolter and V. Koltun. 2018. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling.

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