Research on AIS Data Analysis and Its Integration into Discipline-specific Teaching

Yue Yan; Enze Wu; Taizhi Lv

doi:10.54097/169bh754

Authors

Yue Yan
Enze Wu
Taizhi Lv

DOI:

https://doi.org/10.54097/169bh754

Keywords:

Shipping industry, Digital transformation, AIS, Data analysis, Big data technology

Abstract

The processing of ship AIS (Automatic Identification System) data plays a crucial role in the digital transformation of the shipping industry. From data collection to data storage, data analysis, and data visualization, AIS data processing technology provides robust support for the shipping sector. In terms of data collection, the platform utilizes distributed message queues to aggregate AIS data. For data storage, the platform efficiently manages and stores massive amounts of AIS data using data warehouses. When it comes to data analysis, the platform mines and analyzes AIS data through Lambda, extracting valuable information such as ship trajectories and sailing speeds, thereby supporting areas such as ship management, navigation safety, and marine research. In data visualization, the platform intuitively displays ship navigation status through charts and maps, providing strong support for decision-making. As an essential component of big data applications, AIS data analysis technology is playing an increasingly important role in the shipping industry. The big data technology major of Jiangsu Maritime Institute integrates big data applications like AIS data analysis into its curriculum, taking the upgrading needs of the shipping industry as an entry point. It actively plans the cultivation of composite big data skilled talents under the backdrop of smart shipping, comprehensively serving the upgrading of the shipping industry and promoting the transformation from a major shipping country to a shipping powerhouse.

References

[1] Aiello, Giuseppe, Antonio Giallanza, and Giuseppe Mascarella. "Towards Shipping 4.0. A preliminary gap analysis." Procedia Manufacturing 42 (2020): 24-29.

[2] Yang, Dong, et al. "How big data enriches maritime research–a critical review of Automatic Identification System (AIS) data applications." Transport Reviews 39.6 (2019): 755-773.

[3] Ribeiro, Claudio V., Aline Paes, and Daniel de Oliveira. "AIS-based maritime anomaly traffic detection: A review." Expert Systems with Applications 231 (2023): 120561.

[4] Lee, Hyeong-Tak, et al. "An AIS data-driven approach to analyze the pattern of ship trajectories in ports using the DBSCAN algorithm." Applied sciences 11.2 (2021): 799.

[5] Xiao, Ye, et al. "Adaptive multi-source data fusion vessel trajectory prediction model for intelligent maritime traffic." Knowledge-Based Systems 277 (2023): 110799.

[6] Yang, Cheng-Hong, et al. "AIS-based intelligent vessel trajectory prediction using bi-LSTM." IEEE Access 10 (2022): 24302-24315.

[7] Rong, H., A. P. Teixeira, and C. Guedes Soares. "Ship collision avoidance behaviour recognition and analysis based on AIS data." Ocean Engineering 245 (2022): 110479.

[8] You, Bin, et al. "Ship collision avoidance behavior analysis based on AIS data." 2021 6th International Conference on Transportation Information and Safety (ICTIS). IEEE, 2021: 787-793.

[9] Zhang, Jinfen, et al. "An interpretable knowledge-based decision support method for ship collision avoidance using AIS data." Reliability Engineering & System Safety 230 (2023): 108919.

[10] Liu, Zhao, et al. "A quantitative method for the analysis of ship collision risk using AIS data." Ocean Engineering 272 (2023): 113906.

[11] Vicente-Cera, Isaías, et al. "Use of AIS data for the environmental characterization of world cruise ship traffic." International Journal of Sustainable Transportation 14.6 (2020): 465-474.

[12] Asborno, Magdalena I., et al. "Inland waterway network mapping of AIS data for freight transportation planning." The Journal of Navigation 75.2 (2022): 251-272.

[13] Serra-Sogas, Norma, et al. "Using aerial surveys to fill gaps in AIS vessel traffic data to inform threat assessments, vessel management and planning." Marine Policy 133 (2021): 104765.

[14] Peddireddy, Kiran. "Streamlining Enterprise Data Processing, Reporting and Realtime Alerting using Apache Kafka." 2023 11th International Symposium on Digital Forensics and Security (ISDFS). IEEE, 2023: 1-4.

[15] Zafeirakopoulos, Alexandros Nikolaos, and Euripides GM Petrakis. "HYAS: hybrid autoscaler agent for apache Flink." International Conference on Web Engineering. Cham: Springer Nature Switzerland, 2023: 34-48.

[16] Liao, Xuehua, et al. "Redis-based full-text search extensions for relational databases." International Journal of Machine Learning and Cybernetics (2024): 1-17.

[17] Chae, Sangeun, et al. "Optimizing Read Performance of HBase through Dynamic Control of Data Block Sizes and KVCache." Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing. 2024: 1495-1503.