| People | Locations | Statistics |
|---|---|---|
| Ziakopoulos, Apostolos | Athens |
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| Vigliani, Alessandro | Turin |
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| Catani, Jacopo | Rome |
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| Statheros, Thomas | Stevenage |
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| Utriainen, Roni | Tampere |
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| Guglieri, Giorgio | Turin |
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| Martínez Sánchez, Joaquín |
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| Tobolar, Jakub |
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| Volodarets, M. |
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| Piwowar, Piotr |
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| Tennoy, Aud | Oslo |
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| Matos, Ana Rita |
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| Cicevic, Svetlana |
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| Sommer, Carsten | Kassel |
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| Liu, Meiqi |
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| Pirdavani, Ali | Hasselt |
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| Niklaß, Malte |
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| Lima, Pedro | Braga |
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| Turunen, Anu W. |
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| Antunes, Carlos Henggeler |
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| Krasnov, Oleg A. |
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| Lopes, Joao P. |
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| Turan, Osman |
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| Lučanin, Vojkan | Belgrade |
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| Tanaskovic, Jovan |
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Wrobel, Krzysztof
Gdynia Maritime University
in Cooperation with on an Cooperation-Score of 37%
Topics
- autonomous vehicle
- driver
- safety
- implementation
- automobile industry
- market
- alertness
- automation
- shipping
- iron
- autonomous vehicle handover
- market survey
- market research
- assessment
- data
- researcher
- financing
- guideline
- sea
- ocean
- algorithm
- forecasting
- weight
- passenger
- indicating instrument
- vehicle occupant
- vehicle fleet
- bridge
- prevention
- big data
- data analysis
- marine safety
- passenger ship
- identification system
- bow
- autonomous ship
- water transportation crash
- planning
- design
- supervision
- environmental policy
- waterway
- hydrography
- water traffic
- ship
- industry
- crash
- density
- decision support system
- routing
- validation
- traffic density
- face
- supporting
- computer science
- mechanical engineering
- software
- profit
- system safety
- recommendation
- safety analysis
- developer
- ocean engineering
- oceanography
- structural engineering
- construction engineering
- risk assessment
- hazard
- transportation safety
- shipbuilding
- economic condition
- autumn
- history
- politics
- collapse
- maritime industry
- shipyard
- 21st century
- infrastructure
- gas
- submarine
- geotechnical engineering
- wind
- expansion
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- pipeline transportation
- coast
- fossil fuel
- farm
- underwater pipeline
- oil field
- theory
- computer vision
- vision
- velocity
- intelligence
- artificial intelligence
- positioning
- engineering
- position fixing
- dynamic positioning
- show 71 more
Publications
- 2022A Cross-Domain Scientometric Analysis of Situational Awareness of Autonomous Vehicles With Focus on the Maritime Domaincitations
- 2022Know your safety indicator – A determination of merchant vessels Bow Crossing Range based on big data analyticscitations
- 2021With Regard to the Autonomy in Maritime Operations – Hydrography and Shipping, Interlinkedcitations
- 2020A bibliometric analysis and systematic review of shipboard Decision Support Systems for accident preventioncitations
- 2019Preliminary Results of a System-theoretic Assessment of Maritime Autonomous Surface Ships’ Safetycitations
- 2018Challenges, solution proposals and research directions in safety and risk assessment of autonomous shipping
- 2017Towards the assessment of potential impact of unmanned vessels on maritime transportation safetycitations
- 2016Fall and Rise of Polish Shipbuilding Industry
- 2015Specificity of Geotechnical Measurements and Practice of Polish Offshore Operations
- 2014SLAM - Based Approach to Dynamic Ship Positioning
Places of action
article
Know your safety indicator – A determination of merchant vessels Bow Crossing Range based on big data analytics
Abstract
Funding Information: The study described has been performed as part of the Detection, prediction, and solutions for safe operations of MASS (ENDURE) project (number NOR/POLNOR/ENDURE/0019/2019–00), supported by the Polish National centre for Research and Development and financed by Research Council of Norway. The authors are grateful to Danish Maritime Authority for making the AIS data available for analysis. Funding Information: The study described has been performed as part of the Detection, prediction, and solutions for safe operations of MASS (ENDURE) project (number NOR/POLNOR/ENDURE/0019/2019?00), supported by the Polish National centre for Research and Development and financed by Research Council of Norway. The authors are grateful to Danish Maritime Authority for making the AIS data available for analysis. Publisher Copyright: © 2021 The Author(s) ; Even in the era of automatization maritime safety constantly needs improvements. Regardless of the presence of crew members on board, both manned and autonomous ships should follow clear guidelines (no matter as bridge procedures or algorithms). To date, many safety indicators, especially in collision avoidance have been proposed. One of such parameters commonly used in day-to-day navigation but usually omitted by researchers is Bow Crossing Range (BCR). Therefore, this paper aims to investigate, what are typical, empirical values of BCR during routine operations of merchant ships, as well as investigate what factors impact this indicator and to what extent. To this end, a ten-year big dataset of real maritime traffic obtained from the Automatic Identification System (AIS) was used to provide statistical and spatiotemporal analyses. The results indicate that BCR is strongly related to the type of navigational area (open sea or restricted waters) but not with the dimensions or speed of ships. Among analyzed vessel types, passenger ships were noted as vessels that cross other bows at the closes ranges. Results of this study may be found interesting by fleet ...
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