Adaptive Information Visualization for Maritime Traffic Stream Sensor Data with Parallel Context Acquisition and Machine Learning

Excessive information significantly increases the mental burden on operators of critical monitoring services such as maritime and air traffic control. In these fields, vessels and aircraft have sensors that transmit data to a control center. Because of the large volume of collected data, it is infea...

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Vydané v:Sensors (Basel, Switzerland) Ročník 19; číslo 23; s. 5273
Hlavní autori: Kim, Kwang-il, Lee, Keon Myung
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 29.11.2019
MDPI
Predmet:
Information overload
Information systems
Machine learning
Questionnaires
Roads & highways
Sensors
Traffic control
Wireless networks
stream data
vessel traffic service
maritime traffic stream sensor data
context-aware service
big data
distributed and parallel processing
ISSN:1424-8220, 1424-8220
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Shrnutí:Excessive information significantly increases the mental burden on operators of critical monitoring services such as maritime and air traffic control. In these fields, vessels and aircraft have sensors that transmit data to a control center. Because of the large volume of collected data, it is infeasible for monitoring stations to display all of the information on monitoring screens that have limited sizes. This paper proposes a method for automatically selecting maritime traffic stream data for display from a large number of candidates in a context-aware manner. Safety is the most important concern in maritime traffic control, and special care must be taken to avoid collisions between vessels at sea. It presents an architecture for an adaptive information visualization system for a maritime traffic control service. The proposed system adaptively determines the information to be displayed based on the safety evaluation scores and expertise of vessel traffic service operators. It also introduces a method for safety context acquisition to assess the risk of collisions between vessels, using parallel and distributed processing of maritime stream data transmitted by sensors on the vessels at sea. It provides an information-filtering, knowledge extraction method based on the work logs of traffic service operators, using a machine learning technique to generate a decision tree. We applied the proposed system architecture to a large dataset collected at a port. Our results indicate that the proposed system can adaptively select traffic information according to port conditions and to ensure safety and efficiency.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s19235273