A deep learning approach for port congestion estimation and prediction-Reference-Cited by-同舟云学术

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A deep learning approach for port congestion estimation and prediction

Published:2022-04-07 Issue: Volume: Page:1-26
ISSN:0308-8839
Container-title:Maritime Policy & Management
language:en
Short-container-title:Maritime Policy & Management

Author:

Peng Wenhao¹,Bai Xiwen¹,Yang Dong²^ORCID,Yuen Kum Fai³^ORCID,Wu Junfeng⁴

Affiliation:

1. Department of Industrial Engineering, Tsinghua University, Beijing, CY, China

2. Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hong Kong, CY, China

3. School of Civil and Environmental Engineering, Nanyang Technological University, Singapore, Singapore

4. Shanghai WinJoin Information Technology Co Ltd, Shanghai, CY, China

Funder

National Science Foundation of China

Tsinghua University Initiative Scientific Research Program

Publisher

Informa UK Limited

Subject

Management, Monitoring, Policy and Law,Ocean Engineering,Transportation,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/03088839.2022.2057608

Reference36 articles.

1. Port congestion and the economics of LPG seaborne transportation

2. A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data

3. Container Port Performance Measurement and Comparison Leveraging Ship GPS Traces and Maritime Open Data

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