Crossing behavior decision-making for inland ferry ships based on Machine Learning-Reference-Cited by-同舟云学术

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Crossing behavior decision-making for inland ferry ships based on Machine Learning

Published:2021-10-22 Issue: Volume: Page:
ISSN:
Container-title:2021 6th International Conference on Transportation Information and Safety (ICTIS)
language:
Short-container-title:

Author:

Yuan Xiaoli¹,Zhang Di¹,Zhang Jinfen²,Cai Mingyou¹,Zhang Mingyang³

Affiliation:

1. School of Transportation and Logistics Engineering, Wuhan University of Technology National Engineering Research Center for Water Transport Safety, Wuhan University of Technology,Wuhan,China

2. Intelligent Transportation Systems Center, Wuhan University of Technology National Engineering Research Center for Water Transport Safety, Wuhan University of Technology,Wuhan,China

3. Aalto University, School of Engineering,Department of Mechanical Engineering,Finland

Funder

National Key Technologies Research & Development Program

International S&T Cooperation Program of China

National Natural Science Foundation of China

Hubei Provincial Natural Science Foundation of China

Technical Innovation Project of Jiangsu Province

European Union's Horizon 2020 research and innovation programme

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9798405/9798406/09798496.pdf?arnumber=9798496

Reference30 articles.

1. Machine learning and data-driven fault detection for ship systems operations

2. A comparative CO2 emissions analysis and mitigation strategies of short-sea shipping and road transport in the Marmara Region

3. A risk model of passenger ship fire safety and its application

4. A Risk Management Procedure for the Washington State Ferries

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Study on real-time collision avoidance decision-making under 5G environment;2023 7th International Conference on Transportation Information and Safety (ICTIS);2023-08-04

2. Path Planning for Ferry Crossing Inland Waterways Based on Deep Reinforcement Learning;Journal of Marine Science and Engineering;2023-02-03

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