Submission Deadline: 28 February 2027 View: 57 Submit to Special Issue
Prof. Maohan Liang
Email: mhliang@whut.edu.cn
Affiliation: School of Navigation, Wuhan University of Technology, Wuhan, China
Research Interests: maritime traffic data mining, AIS data analytics; vessel trajectory reconstruction, maritime intelligent transportation systems, maritime safety and risk assessment, spatiotemporal data mining, machine learning
Dr. Qin Zhou
Email: q.zhou@soton.ac.uk
Affiliation: Department of Decision Analytics and Risk, Southampton Business School, University of Southampton, Southampton, United Kingdom
Research Interests: data-driven decision making in closed-loop supply chains and remanufacturing authorization
Prof. Ryan Wen Liu
Email: wenliu@whut.edu.cn
Affiliation: School of Navigation, Wuhan University of Technology, Wuhan, China
Research Interests: maritime big data, AIS data mining, vessel trajectory analysis, intelligent transportation systems, maritime safety, semantic web, knowledge engineering, smart shipping
With the rapid development of computing and information technologies, the increasing requirements in maritime traffic management and services have dramatically accelerated the evolution of maritime traffic data mining technologies. It has attracted growing interest to explore advanced technologies for analysing big maritime data and precising the understanding of maritime traffic and operation patterns and routines. Maritime big data drives better intelligence and decision making for situational awareness applications in maritime sector, which makes it feasible for a human or machine to better perceive the complex environment of maritime transportation by taking full advantage of data mining and knowledge discovery. The extracted knowledge could guarantee traffic safety and efficiency for both human activities and unmanned/autonomous maritime vehicles.
Though significant progresses have been achieved in maritime traffic data mining, both industry and academia are still facing several major challenges in computing algorithms, infrastructures and systems which hinder further development of maritime traffic data mining. There are several challenges that should be addressed: how to improve the maritime traffic data quality; how to promote maritime traffic data mining through advanced artificial intelligence and machine learning techniques; how to develop analysis, simulation and prediction methods in big data-driven smart maritime systems; how to enhance service quality and reliability with system engineering and information & communication technologies; finally, how to develop task-specific computational methods and systems to handle intelligent maritime applications.
This special issue mainly focuses on the current and emerging topics in maritime traffic data mining, which addresses the original theoretical development and practical applications. We especially welcome researchers from both industry and academia for presenting their ongoing work, relevant research outcomes and experiences gained, and the state-of-the-art computational systems, techniques and methodologies as well as practical applications for maritime traffic data.
Potential topics include, but are not limited to, the following:
- Maritime traffic data processing theory and methods (e.g., collection, reconstruction, compression, segmentation, clustering, and classification, etc.)
- AI and machine learning methods for big maritime data mining
- Parallel and concurrent computing systems for big maritime data processing
- Visualization of spatiotemporal maritime data
- Maritime traffic knowledge engineering
- Maritime traffic safety assessment and management
- Maritime traffic data analysis, simulation and prediction
- Multi-sensor perceptual data collection, fusion, and analysis
- Blockchain-based maritime internet of things
- Fog/Edge computing-based maritime applications
- Data-driven maritime traffic awareness and prediction
- Data-driven modelling and simulation of ship behaviour
- Data-driven decision making to promote vessel traffic services
- Data-driven maritime anomaly detection
- Data-enabled maritime spatial planning
- Global maritime trade network
- Reliability and safety of maritime engineering
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