Analysis of Big Data on New Technologies for Port Safety Management in Preparation for Eco-Friendly and Digital Paradigm Transformation
Source abstract
Abstract
Ports serve as key nodes in eco-friendly and digital logistics networks, and the volume of cargo handled continues to increase in response to growing international trade. However, the increased workload within limited spaces heightens the risk of safety accidents, and the number of casualties in port stevedoring operations has continued to rise. As the era of transition toward eco-friendly and digital paradigms unfolds, the adoption of new technologies in ports presents a strategic opportunity to enhance safety management. As of 13 May 2025, the study conducted a text-mining analysis based on research abstracts related to the keyword “New technology and port safety,” in the context of internal and external environmental changes. Specifically, a total of 639 research abstracts were collected, but 138 abstracts, which were unrelated to port safety, were excluded, and 501 abstracts from the Clarivate Web of Science database were analyzed, focusing on 2676 words that appeared at least twice. The study applied Term Frequency (TF) analysis, TF–Inverse Document Frequency analysis, Semantic Network Analysis, and Topic Modeling. The results indicate that Internet of Things emerged as a core solution for strengthening port safety management. However, challenges remain, including the prevention of security breaches, high infrastructure implementation costs, and limitations in battery life.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Economic and Technological Systems Analysis · Maritime Ports and Logistics · Economic and Technological Developments in Russia
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Convergence Interdisciplinary Education, National Korea Maritime and Ocean University, Busan 49112, Republic of Korea
College of Maritime Sciences, National Korea Maritime and Ocean University, Busan 49112, Republic of Korea
Logistics System Engineering, National Korea Maritime and Ocean University, Busan 49112, Republic of Korea
Funding
Ministry of SMEs and Startups
References (53)
Show source-provided references
1. Parra, N.M., Nagi, A., and Kersten, W. (2025, April 23). Risk Assessment Methods in Seaports. Publications of the HAZARD Project. 2018, Volume 24. Available online: https://www.researchgate.net/publication/331354975_Risk_Assessment_Methods_in_Seaports_A_Literature_Review.
2. Kusuma, L.T.W.N., and Tseng, F.-S. (2019). Analysis of the Impact of the “Sea Toll” Program for Seaports: Resilience and Competitiveness. Appl. Sci., 9.10.3390/app9163407 ↗
3. Review on the Role of Ports in the Development of a Nation10.1016/j.aqpro.2015.02.040 ↗
4. Foreland-Based Regionalization: Integrating Intermediate Hubs with Port Hinterlands10.1016/j.retrec.2009.12.004 ↗
5. A Critical Review of Conventional Terminology for Classifying Seaports10.1016/j.tra.2004.11.003 ↗
6. Cybersecurity in Ports and Maritime Industry: Reasons for Raising Awareness on This Issue10.1016/j.tranpol.2020.10.001 ↗
7. Seaport Innovation Trends: Global Insights10.1016/j.marpol.2023.105585 ↗
8. Human Factors and Safety Culture: Challenges and Opportunities for the Port Environment10.1016/j.ssci.2018.03.008 ↗
9. Risk Assessment and Mitigation for Better Safety: Case Study of Kemaman Port10.46754/umtjur.v3i2.206 ↗
10. MFMDepth: MetaFormer-Based Monocular Metric Depth Estimation for Distance Measurement in Ports10.1016/j.cie.2025.111325 ↗
11. Maintaining and Researching Port Safety: A Case Study of the Port of Kaohsiung10.1007/s12544-017-0250-z ↗
12. Enhancing Safety Performance in Seaport: A Scientometric and Scoping Analysis of Performance Evaluation10.55248/gengpi.5.0724.2009 ↗
13. Disruption Risks and Mitigation Strategies: An Analysis of Asian Ports10.1080/03088839.2015.1016560 ↗
14. Occupational Risks and Health and Safety Management Strategies in the Port Sector: A Systematic Literature Review10.1016/j.ssci.2024.106767 ↗
15. The Importance of Communication for the Maintenance of Health and Safety in Work Operations in Ports10.1016/j.ssci.2017.03.020 ↗
16. Maritime Risk in Seaport Operation: A Cross-Country Empirical Analysis with Theoretical Foundations10.1016/j.ajsl.2018.09.010 ↗
17. Healthcare Maintenance and Safety Policy: Evidence from Port Harcourt Seaport
18. A Text-Mining-Based Patent Network: Analytical Tool for High-Technology Trend10.1016/j.hitech.2003.09.003 ↗
19. Text Mining Techniques for Patent Analysis10.1016/j.ipm.2006.11.011 ↗
20. Selection Criteria for Text Mining Approaches10.1016/j.chb.2014.10.062 ↗
21. Decision Making in the Process of Ensuring Safety within Seaport Area10.1016/j.procs.2024.09.705 ↗
22. Kaup, M., Łozowicka, D., Baszak, K., Ślączka, W., and Kalbarczyk-Jedynak, A. (2022). Risk Analysis of Seaport Construction Project Execution. Appl. Sci., 12.10.3390/app12168381 ↗
23. An Advanced Risk Analysis Approach for Container Port Safety Evaluation10.1080/03088839.2014.960498 ↗
24. An Integrated Fuzzy Risk Assessment for Seaport Operations10.1016/j.ssci.2014.04.001 ↗
25. Identifying Public Transit Deserts: A Travel Demand-Independent Persistent Homology-Based Method10.1093/tse/tdaf015 ↗
26. Liu, W., Cheng, L., Liu, Z., Yang, Y., and Li, L. (2021, January 20–22). The Development of Port Safety Training Platform Based on Virtual Reality Technology. Proceedings of the 2021 IEEE 7th International Conference on Virtual Reality (ICVR), Foshan, China.10.1109/ICVR51878.2021.9483808 ↗
27. An RFID Model for Improving Workers’ Safety at the Seaport in Transitional Environment10.3846/16484142.2016.1233512 ↗
28. Internet of Things for Smart Ports: Technologies and Challenges10.1109/MIM.2018.8278808 ↗
29. The Internet of Things for Smart Ports10.1016/j.procs.2022.07.123 ↗
30. The Future of IoT Applications in Port Management System in Malaysia
31. Strategies and Approach for Smart City–Port Ecosystems Development Supported by the Internet of Things10.3846/transport.2021.16194 ↗
32. Some ICT Systems for Increasing Occupational Safety with a Reference to the Seaport Environment10.17818/NM/2018/2.5 ↗
33. Li, Y., Zhu, P., Zhang, G., and Yu, Y. (2023). Improving Seaport Wharf Maintenance and Safety with Structural Health Monitoring System in High Salt and Humidity Environments. Sustainability, 15.10.3390/su15054472 ↗
34. Issa, M., Rizk, P., Boulon, L., Rezkallah, M., Rizk, R., and Ilinca, A. (2025). Smart, Connected, and Sustainable: The Transformation of Maritime Ports Through Electrification, IoT, 5G, and Green Energy. Sustainability, 17.10.3390/su17177568 ↗
35. Jović, M., Tijan, E., Aksentijević, S., and Čišić, D. (2019, January 20–24). An Overview of Security Challenges of Seaport IoT Systems. Proceedings of the 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia.10.23919/MIPRO.2019.8757206 ↗
36. A Holistic View of Maritime Navigation Accidents and Risk Indicators: Examining IMO Reports from 2011 to 202110.1186/s41072-023-00135-y ↗
37. A Study on Trends of Key Issues in Port Safety at Busan Port
38. Word Frequency-Content Analysis Approach to Identify the Terms in the Content of Library Rules and Regulations of Indian Universities and European Universities: A Comparative Study
39. A Global Supply Chain Risk Management Framework: An Application of Text-Mining to Identify Region-Specific Supply Chain Risks10.1016/j.aei.2020.101053 ↗
40. Text Mining Techniques to Capture Facts for Cloud Computing Adoption and Big Data Processing10.1109/ACCESS.2019.2950045 ↗
41. Exploring Diseases and Syndromes in Neurology Case Reports from 1955 to 2017 with Text Mining10.1016/j.compbiomed.2019.04.008 ↗
42. Predicting Stock Trend Using an Integrated Term Frequency–Inverse Document Frequency-Based Feature Weight Matrix with Neural Networks10.1016/j.asoc.2020.106684 ↗
43. Sustainability Trends in the Process Industries: A Text Mining-Based Analysis10.1016/j.compind.2014.01.004 ↗
44. Smith, A. (June, January 27). Automatic Extraction of Semantic Networks from Text Using Leximancer. Proceedings of the Companion Volume of the Proceedings of HLT-NAACL 2003-Demonstrations, Edmonton, AB, Canada.
45. Cenek, M., Bulkow, R., Pak, E., Oyster, L., Ching, B., and Mulagada, A. (2019). Semantic Network Analysis Pipeline—Interactive Text Mining Framework for Exploration of Semantic Flows in Large Corpus of Text. Appl. Sci., 9.10.3390/app9245302 ↗
46. Semantic Network Analysis as a Method for Visual Text Analytics10.1016/j.sbspro.2013.05.053 ↗
47. Approaching Fashion Design Trend Applications Using Text Mining and Semantic Network Analysis10.1186/s40691-020-00221-w ↗
48. An Analysis on Text Mining Techniques for Smart Literature Review10.30534/ijatcse/2021/1121022021 ↗
49. Discovering Topics and Trends in the Field of Artificial Intelligence: Using LDA Topic Modeling10.1016/j.eswa.2023.120114 ↗
50. Maier, D., Waldherr, A., Miltner, P., Wiedemann, G., Niekler, A., Keinert, A., Pfetsch, B., Heyer, G., Reber, U., and Häussler, T. (2021). Applying LDA Topic Modeling in Communication Research: Toward a Valid and Reliable Methodology. Computational Methods for Communication Science, Routledge.
51. Content Analysis of E-Petitions with Topic Modeling: How to Train and Evaluate LDA Models?10.1016/j.ipm.2018.05.006 ↗
52. Latent Dirichlet Allocation
53. Tsujii, J., Henderson, J., and Paşca, M. (2012, January 12–14). Exploring Topic Coherence over Many Models and Many Topics. Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Jeju Island, Republic of Korea.