Exploring the Underlying Causes of Chinese Eastern Star, Korean Sewol, and Thai Phoenix Ferry Accidents by Employing the HFACS-MA

Water search and rescue (SAR) for ship accidents in China: analysis of 12 years' data

Background

Maritime search and rescue (SAR) remains a great global challenge because of the long distances, harsh environment and complicated trauma. A systematic investigation and analysis of China Maritime Search and Rescue Center (CMSRC) data has been lacking. This study aimed to provide more insightful information for future development of a better maritime and aquatic SAR system in China.

Methods

This retrospective study retrieved and analyzed data on the water traffic volume from The Ministry of Transportation, People's Republic of China website and SAR data on ship accidents between January 1, 2008 and December 31, 2019. Spearman's correlation test was performed to analyze the data for the number of ship accidents, number of persons in distress, number of rescued persons, SAR success rate, and SAR forces. The χ² test was used to assess significant changes in the proportion of ship accident locations, categories, dispatched SAR forces, and location of deaths annually. The Cox Stuart test was applied to determine the trends in the data from 2008 to 2019.

Results

Between 2008 and 2019, a total of 24,013 ship accidents were reported and recorded by the CMSRC surveillance system; 209,948 persons in distress because of ship accidents were reported; 8,051 individual deaths from ship accidents. Water traffic volume and ship carrying capacity increased while the annual number of persons in distress, ship collisions, and ship collision-related deaths decreased. The SAR success rate (96.17%±0.92%) did not improve despite an increase in the number of rescue ships dispatched during this period. Helicopters (92.40±20.58 min) arrived faster than rescue ships (283.75±40.96 min) but the dispatched number of helicopters did not increase during this period. The average arrival time of nearby passing ships (41.90±7.98 min) was the shortest.

Conclusions

CMSRC efforts mitigated the growth of ship accidents despite increasing traffic volume. More dispatched rescue ships did not improve the SAR success rate without saving arrival time. Future SAR protocols may need to increase and strengthen the role of helicopters and nearby passing ships to improve the SAR success rate.

Cause Analysis of Unsafe Acts of Pilots in General Aviation Accidents in China with a Focus on Management and Organisational Factors

Objectives. General aviation (GA) safety has become a key issue worldwide and pilot errors have grown to be the primary cause of GA accidents. However, fewer empirical studies have examined the contribution of management and organizational factors for these unsafe acts. Flawed decisions at the organizational level have played key roles in the performance of pilots. This study provides an in-depth understanding of the management and organizational factors involved in GA accident reports. Methods. A total of 109 GA accidents in China between 1996 and 2021 were analysed. Among these reports, pilot-related accidents were analysed using the human factors analysis and classification system (HFACS) framework. Results. The significant effects of managerial and organizational factors and the failure pathways on GA accidents have been identified. Furthermore, unlike traditional HFACS-based analyses, the statistically significant relationships between failures at the organizational level and the sub-standard acts of the pilots in GA accidents were revealed. Conclusions. Such findings support that the GA accident prevention strategy that attempts to reduce the number of unsafe acts of pilots should be directed to the crucial causal categories at HFACS organizational levels: resource management, organizational process, failure to correct a known problem, inadequate supervision and supervisory violations.

Shipping Accidents Dataset: Data-Driven Directions for Assessing Accident’s Impact and Improving Safety Onboard

Recent tragic marine incidents indicate that more efficient safety procedures and emergency management systems are needed. During the 2014–2019 period, 320 accidents cost 496 lives, and 5424 accidents caused 6210 injuries. Ideally, we need historical data from real accident cases of ships to develop data-driven solutions. According to the literature, the most critical factor to the post-incident management phase is human error. However, no structured datasets record the crew’s actions during an incident and the human factors that contributed to its occurrence. To overcome the limitations mentioned above, we decided to utilise the unstructured information from accident reports conducted by governmental organisations to create a new, well-structured dataset of maritime accidents and provide intuitions for its usage. Our dataset contains all the information that the majority of the marine datasets include, such as the place, the date, and the conditions during the post-incident phase, e.g., weather data. Additionally, the proposed dataset contains attributes related to each incident’s environmental/financial impact, as well as a concise description of the post-incident events, highlighting the crew’s actions and the human factors that contributed to the incident. We utilise this dataset to predict the incident’s impact and provide data-driven directions regarding the improvement of the post-incident safety procedures for specific types of ships.