During the first 30 months of the project, the following research work has been performed through the development by the TRUST team of 4 postdoctoral researchers, 6 PhD students and 5 visiting scholars:
1. A comprehensive climate risk assessment for CSCs, involving collecting relevant data, analysing existing studies, and developing a classification method. The findings guide future research and strategies for mitigating climate risks.
2. Development of new CSC integration modelling using centrality and transport network analysis approaches.
3. Investigation of risk estimation and uncertainty treatment approaches in the literature.
4. Identification and modelling of security risk measures, with particular reference to maritime CSC, such as ships and port terminals.
5. Development of analytical models to assess the perception of various stakeholders in CSCs regarding how climate change risks would impact supply chain facilities and operations.
6. Development of advanced climate risk and resilience indexes for different transport modes along CSCs.
7. Collection CSC security risk data and development of two major databases from both cyber and physical security perspectives.
8. Analysis of CSC security risks using advanced uncertainty modelling (e.g. cargo theft and maritime piracy)
9. Development of historical safety risk database from reputable sources, such as the Global Integrated Shipping Information System (GISIS) and Lloyd’s Register Fairplay (LRF).
10. Development of a new database focusing on human factors and human failures in maritime transport using maritime accident reports (e.g. Maritime Accident Investigation Branch).
11. Hazard identification and risk assessment of emerging technologies (e.g. autonomous ships) influencing CSC safety.
12. A new framework for big data mining to extract patterns and transport routes in CSCs.
13. Development of spatio-temporal pattern analysis models to understand and prevent maritime security risks (e.g. piracy incidents).
14. Development of new uncertainty modelling to predict global maritime accidents.
15. Establishment of new benchmarks for ship trajectory prediction models for anti-collision of autonomous ships.
16. A new data-driven risk model enabling the systematic analysis of the risk factors of maritime accidents in restricted waters.
17. A new safety and security co-analysis (SSCA) framework using security-driven and safety-oriented methods.
18. A new risk model defines risk parameters of risk events from multi-perspective to facilitate the modelling and understanding of risks in CSCs.
19. Development of a risk evolution analysis model to investigate the ship collision risk in pilotage process using Dynamic Bayesian Network (DBN).
20. A novel maritime cybersecurity risk analysis method under high uncertainty for quantitative analysis and prioritisation of the risk levels of maritime cyber threats.
21. A new tool to analyse seafarer competencies to rationalise human factor evaluation in the maritime closed-loop system and reflect the dynamic human-machine cooperation process.
22. A conceptual framework for assessing seafarer psychological factors using neurophysiological analysis to quantitatively assess the psychological factor and test, verify, and train seafarers' behaviours for ship safety at sea and along coasts.
23. Investigation of impact of specific parameters on human factors through a psychological mean. For instance, the relationship between marine engine noise and objective sleep parameters is studied to measure the degree of seafarer fatigue.
24. Development of new human reliability analysis methods using big data and artificial intelligence technologies (e.g. haemoglobin data and artificial neural network).
25. Establishment of a new framework for the quantitative analysis of human factors in maritime transport, involving different research objectives, bespoke approaches aiming at specific purposes, and human reliability performance evaluation for a particular task.
During the project period, the project team has produced 49 technical papers, (i.e. 38 journal, and 11 conference publications) and organised 2 technical workshop sessions through the project (at Liverpool UK, Hong Kong (online)). The team has won a few awards based on the new findings, including LJMU VC Award for Excellence in Research and Knowledge Transfer (2021) and ICASL Conference Best Paper Award (2021). The research has generated a huge impact on the understanding on how CSC can be designed and operated in a resilient and sustainable environment.
