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Considering the unobserved spatial heterogeneity, this study aims to build a Bayesian spatial multinomial logistic (BSMNL) model by utilizing the geographic information from historical maritime accidents. The proposed BSMNL model can be applied to investigate the determinants of human errors involved in maritime accidents. Compared to the traditional multinomial logistic (MNL) model, the proposed BSMNL model produces a more accurate estimate of the effects of environmental and accident factors on the occurrence likelihood of human errors in maritime accidents. Results show that accidents involving cargo and container ships; tankers carrying liquefied natural gas (LNG), liquefied petroleum gas (LPG), or oil; and fishing vessels are more likely to be associated with human errors. Further, one important finding is that the involvement of fishing vessels significantly increases the occurrence probability of both negligence errors and judgment or operational errors. In addition, the occurrence likelihood of human errors is generally higher in springtime, conditions of poor visibility, the absence of strong winds or waves, and the moored or docked status. [ABSTRACT FROM AUTHOR]

Considering the differences in traffic delays and accident costs at different stages of the shipping cycle, this study develops a system dynamics model to determine the impacts of the shipping cycle on traffic delays and accident costs at port collector-distributor roads. Taking the collector-distributor roads of Yangshan Deepwater Port in Shanghai as a case study, parameter estimation and a model validity test are conducted. The changes in traffic delays and accident costs before and during maintenance activities at different stages of the shipping cycle are analyzed by increasing the growth rates of gross domestic product (GDP), and secondary and tertiary industries in GDP. The model results show that, as the economic growth rate increases, the growth magnitudes of traffic delays and accident costs during a peak period are significantly larger than those during a trough period. The results of this study provide policies and suggestions for improving traffic efficiency and safety on port collector-distributor roads. It is suggested to increase the maintenance frequency and maintenance funds during a peak period. Additionally, it is necessary to develop an intelligent road maintenance system for port collector-distributor roads that takes the shipping cycle into account in order to enhance the road safety level. [ABSTRACT FROM AUTHOR]

The accurate detection of seafarers' unsafe behaviors is of great significance to improve the ship navigation safety. This study proposes an improved deep learning framework with a self-made dataset to detect the unsafe behavior of seafarers on duty. In order to increase the detection speed, the improved Cross Stage Partial connections (CSP) module is proposed to replace the original CSP module in the neck network of traditional algorithm. The efficient channel attention (ECA) module is also introduced to the backbone network of conventional algorithm as the attention mechanism network. In addition, the learning and representation capacities of the improved deep learning framework are promoted by redesigning the sizes of anchor boxes. The experiment results show that the proposed framework outperforms traditional object detection algorithms (e.g., YOLOv5s, R-CNN) in detecting seafarers' unsafe behaviors because of the much higher detection speed and detection accuracy. [ABSTRACT FROM AUTHOR]

Unsafe acts of ship officers have been a direct cause of many maritime accidents. In the maritime industry, much effort has been made to prevent unsafe acts and to improve safe behaviours of ship officers. The positive effect of organizational safety climate on human behaviour has been well documented in the literature. Within this context, this paper aims to pioneer the development of the measurement constructs of safety climate in shipping operations and analyze its impact on safety consciousness and behaviour of ship officers. Using primary data collected from 284 deck officers and captains, an exploratory and confirmatory factor analysis and a structural equation model are used to reveal the relationship among the constructs of safety climate, safety consciousness and safety behaviour. The research results show that (1) safety climate generates a direct positive effect on safety consciousness and behaviour; (2) safety consciousness has a positive effect on safety behaviour; and (3) safety consciousness acts as a mediator between safety climate and safety behaviour. Its managerial implications and practical contributions lie in the provision of useful insights on how ship managers can effectively gain the improved safety behaviours of ship officers by appropriate introduction of safety climate and consciousness. [ABSTRACT FROM AUTHOR]

This paper introduces a scenario evolution model for maritime accidents, wherein Bayesian networks (BNs) were employed to predict the most probable causes of distinct types of maritime incidents. The BN nodes encompass factors such as accident type, life loss contingency, accident severity, quarter and time period of the accident, and type and gross tonnage of the involved ships. An analysis of 5660 global maritime accidents spanning the years 2005 to 2020 was conducted. Using Netica software, a tree augmented network (TAN) model was constructed, thus accounting for interdependencies among risk-influencing factors. To confirm these results, a validation process involving sensitivity analysis and historical accident records was performed. Following this, both forward causal inference and reverse diagnostic inference were carried out on each node variable to scrutinize the accident development trend and evolution process under preset conditions. The findings suggest that the model was competent in effectively predicting the likelihood of various accident scenarios under specific conditions, as well as extrapolating accident consequences. Forward causal reasoning unveiled that general cargo ships with a gross tonnage of 1–18,500 t were most prone to experiencing collision and stranding/grounding accidents in the first quarter. Reverse diagnostic reasoning indicated that, in the early morning hours, container ships, general cargo ships, and chemical ships with a tonnage of 1–18,500 t were less likely to involve life loss in the event of collision accidents. [ABSTRACT FROM AUTHOR]

To enable the reliability theory to be further applied in roadway geometry design and safety evaluation, it is necessary to explore and establish the relationship between the failure index in reliability theory and the traffic accident rate. Based on the collected data, the risk ranking-based verification method and regression prediction model-based verification method considering the probability of driving failure were used to verify the relationship between the probability of driving failure and traffic accident rate. The Spearman's rank correlation coefficient was calculated and the results show that there is a moderate correlation between the driving failure probability of the selected road segment and the traffic accident rate. Four regression prediction models for both two-lane and four-lane roads were established considering the probability of driving failure and four significant variables including segment length, annual average daily traffic volume, speed limit, and curve radius. The results show that the established regression prediction models can fit accident data well. This research verified and established the relationship between the probability of driving failure and the road traffic accident rate and pointed out that the traffic accident rate has a positive correlation with the probability of driving failure. [ABSTRACT FROM AUTHOR]

The underreporting issue on shipping accident data has plagued the researchers focused on maritime safety analysis for many years. For improving the quality of shipping accident records, this study proposes a novel methodology comprising three steps to complement the underreported maritime accident records. The first step is to investigate the underreporting rates under various conditions through questionnaire survey. Based on the survey results, the second step is to build a Cluster-Specific Random Effects (CSRE) model to estimate the underreporting rates under various scenarios. Then, the third step is to replicate the underreported accident records using the Monte Carlo simulation technique. Model results show that the occurrence probability of missing accident records involving liquid cargo ships is lower than other ship categories while fishing ships are more likely to have a higher underreporting rate. Non-serious accidents are more likely to be underreported than serious accidents. The case study confirms the effectiveness of the proposed three-step method for complementing the maritime accident databases suffering underreporting problems. [ABSTRACT FROM AUTHOR]

Ship anomaly detection is a vital aspect for monitoring navigational safety in specific water areas. Considering the effect of water channel boundaries, we propose the detection of an abnormal ship trajectory based on the complex polygon (DATCP) method to detect ship anomalies in this study. With the automatic identification systems (AIS) data from the Yangtze River estuary, a case study is created to verify the effectiveness of the proposed DATCP method. The case study results reveal that the proposed DATCP method can provide higher detection accuracy than the conventional A* algorithm. The feature analysis results indicate that ship anomalies are significantly influenced by ship type, time period, weather conditions and ship traffic characteristics. [ABSTRACT FROM AUTHOR]

This study investigates the lane-changing behavior in work zone merging areas during the period from the time of starting a lane-changing maneuver to that of completing the lane-changing maneuver. With vehicle trajectory data from short-term work zone sites, we establish a spatial-temporal varying coefficient model considering possible spatial-varying and time-varying effects. Model results show that four factors including the longitudinal gap between the lane-changing vehicle and lead vehicle, lane-changing vehicle speed, through lane lag vehicle type and crash severity between the lane-changing vehicle and the through lane lag vehicle exhibit spatial-varying effects on the drivers' lane-changing behavior, while time-varying effects are found from the lead vehicle speed in the merge lane, crash severities of the lane-changing vehicle with regard to the lead vehicle and the through lane lead vehicle. Time-varying and spatial-varying effects from vehicle crash severities between the lane-changing vehicle and through lane vehicles suggest that lane-changing vehicles tend to take a risky merge as the elapsed time increases or when they are closer to the work zone. [ABSTRACT FROM AUTHOR]