AIS data-driven approach to estimate navigable capacity of busy waterways focusing on ships entering and leaving port
Published 2020 View Full Article
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Title
AIS data-driven approach to estimate navigable capacity of busy waterways focusing on ships entering and leaving port
Authors
Keywords
Automatic identification system, Navigable capacity estimation, Traffic flow, Structural characteristics, Use of marine resources
Journal
OCEAN ENGINEERING
Volume 218, Issue -, Pages 108215
Publisher
Elsevier BV
Online
2020-10-20
DOI
10.1016/j.oceaneng.2020.108215
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- A framework for risk assessment for maritime transportation systems—A case study for open sea collisions involving RoPax vessels
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