Maritime transportation plays a critical role in global trade, and studies on maritime transportation safety management are of great significance to the sustainable development of the maritime industry. Consequently, there has been an increasing trend recently in studies on maritime transportation safety management, especially in terms of safety risk analysis and emergency management. Therefore, the general idea of this article is to provide a detailed literature review of maritime transportation safety management based on 186 articles in the Web of Science (WOS) database published from 2011 to 2022. The purposes of this article are as follows: (1) to provide a statistics-based description and conduct a network-based bibliometric analysis on the basis of the collected articles; (2) to summarize the methodologies/technologies employed in maritime transportation safety management spatiotemporally; and (3) to propose four potential research perspectives in terms of maritime transportation safety management. Based on the findings and insights obtained from the bibliometric and systematic review, the development of a resilient maritime transportation system could be facilitated by means of data- or intelligence-driven technologies, such as scenario representation, digital twinning, and data simulation. In addition, the issues facing intelligent maritime shipping greatly challenge the current maritime safety management system due to the co-existence of intelligent and non-intelligent maritime operation.

To identify and screen the risk scenarios for the navigation risk of intelligent ships, the analysis and evaluation of navigational risks were performed in this study. Risk scenarios were developed and evaluated by mapping the hierarchical holographic modeling (HHM) into risk filtering, ranking and management (RFRM). In detail, considering the insignificant influences of some factors on navigational activities, risk factors were filtered and ranked using the RFRM model. Seven final factors were successfully determined, including traffic flow, navigation environment understanding, ship–shore interaction capabilities, target recognition capabilities, communication equipment reliabilities, professional skills, and situation judgments. The results indicated that cargo security can be guaranteed by following navigational risk identification and screening steps, and thus our findings provide theoretical guidance for the dynamic management of maritime organizations and ship companies. In addition, the proposed methodology is desirable for making predictions on maritime traffic risks.

A high throughput and high sensitivity sensor for detection of oil metal debris is presented. The sensor is mainly composed of a planar coil, a ferrite core and a flat channel. The adoption of flat channel can increase the throughput without sacrifice of sensitivity. The comparative experiments show that the ferrite core not only eliminates the phenomenon of multiple pulse peaks for detecting individual metal particle in the flat channel, but also can improve the sensitivity of inductance detection and resistance detection. For detecting 130-140 μm iron particles, the inductance amplitude and SNR (signal-to-noise ratio, the SNR is the amplitude divided by the interference noise) of sensor with ferrite core are 1.17 times and 1.22 times higher than those of sensor without ferrite core, respectively. For detecting 160-170μm copper particles, the inductance amplitude and SNR of sensor with ferrite core are 1.24 times and 1.28 times higher than those of sensor without ferrite core, respectively, and the resistance amplitude and SNR of sensor with ferrite core are 1.09 times and 1.07 times higher than those of sensor without ferrite core, respectively. In addition, the introduction of resistance detection can increase the ability of sensor to detect non-ferromagnetic metal particles. We can detect iron particles with diameter of 40μm onwards and detect copper particles with diameter of 90μm onwards using the sensor with ferrite core combined with inductance and resistance detection methods. This work improves the throughput of microfluidic high-precision detection, which can provide technical support for large-flow oil detection.