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About the Journal
Superintended by：China North Industries Group Corporation Limited
Sponsoredy：Beijing Institute of Technology, Chinese Scoiety for Environmental Sciences, China Occupational Safety and Health Association
Edited & Published by: Editorial Department of Journal of Safety and Environment
Issues per year: 12
ISSN 1009-6094
CN 11-4537/X
A comprehensive risk assessment method for civil aviation passengers using multi-source data throughout the entire process
YANG Jun;LIU Haoran;WANG Lei;WANG Feiyin;WU Renbiao;Current research on civil aviation passenger risk assessment primarily concentrates on isolated segments or specific indicator characteristics, often neglecting a comprehensive, dynamic evaluation of passenger risk throughout the entire travel process. This paper introduces a dynamic risk assessment method for civil aviation passengers that leverages the integration of multi-source intelligence data. By monitoring the complete passenger journey—including pre-flight, check-in, security checks, and onboard phases—this approach extracts and analyzes multi-dimensional passenger information from the collected data, facilitating a thorough and dynamic assessment of passenger risks. First, a systematic identification of potential risk sources for civil aviation passengers at each security checkpoint has been conducted by performing statistical analyses on the characteristic patterns of risk events from the Aviation Safety Network and the Global Terrorism Database. This process incorporates relevant regulations, industry standards, and official documents, while also considering insights from expert consultations, reviews, and findings from on-site visits and investigations. Second, key characteristic indicators reflecting passenger risk levels were extracted from the identified risk sources, resulting in the development of a comprehensive risk assessment framework consisting of 29 indicators across seven dimensions. The weights of these indicators at various levels were calculated using the Group Analytic Hierarchy Process(GAHP). Subsequently, the risk scenarios within the constructed secondary indicator risk scenario database were quantified, and their comprehensive risk coefficients were computed using the set-valued statistics method. These coefficients were then transformed into membership degree vectors through the application of a trapezoidal membership function. Finally, a dynamic risk assessment model for civil aviation passengers was established based on fuzzy theory, where distinct fuzzy operators were applied at each level to accurately process the evaluation vectors. Validation with passenger cases demonstrated that the model achieved accuracy rates of 100% in identifying low risks and 97.23% in identifying high risks, with an accuracy rate exceeding 90% for medium risks. Overall, the model exhibited robust performance, proving to be an effective auxiliary tool for the risk assessment and classification of civil aviation passengers in real-world operational contexts.
Enhanced credibility FMECA risk analysis for remote digital tower systems
WANG Chao;ZHANG Zeyu;FENG Cun;HOU Ting;To address the challenges of subjective bias and limited discriminative capacity in failure mode prioritization for remote digital tower system safety assessments, this study proposes an enhanced Failure Mode, Effects, and Criticality Analysis(FMECA) methodology. The framework incorporates three key improvements: fuzzy comprehensive evaluation, hybrid weighting, and linear interpolation. First, the fuzzy comprehensive evaluation method is used to quantify expert judgments through a nine-level linguistic scale, converting qualitative assessments into numerical membership matrices. This approach reduces subjectivity by aggregating evaluations from multiple experts. Second, a hybrid weighting strategy is employed, combining subjective weights derived from the Analytic Hierarchy Process(AHP) with objective weights calculated using entropy theory. This balances expert insights with data-driven rigor. Third, linear interpolation refines the discrete Occurrence Probability Rating(OPR) classification, overcoming the granularity limitations present in traditional risk prioritization methods. Experimental validation on 26 typical failure modes demonstrates the effectiveness of the proposed method. The results show that the hybrid weighting strategy reduces severity ranking deviation by 18.6% compared to single-weight approaches, thereby enhancing the objectivity of criticality assessments. The linear interpolation method enhances intra-level discrimination for mid-tier OPR categories by 22.3%, effectively distinguishing between failure modes with similar occurrence probabilities. Key high-risk failure modes, such as panoramic image fusion delays and optical sensor lags, are identified as critical threats to system stability. Additionally, the optimized Risk Priority Number(RPN) model achieves a 34.8% improvement in prioritization accuracy, allowing for clearer differentiation of failure modes with overlapping risk scores in traditional FMECA. In conclusion, the improved FMECA framework systematically addresses the ambiguity and subjectivity inherent in conventional methods. By integrating fuzzy logic, hybrid weighting, and continuous probability scoring, it offers a more robust and granular risk assessment tool for complex systems such as remote digital towers. These advancements provide practical support for optimizing system design, maintenance strategies, and safety management, ultimately enhancing operational reliability in critical aviation infrastructure.
Influence of reward and punishment mechanisms on aviation decision-making among pilots under risk
PENG Xing;WANG Xiang;ZHONG Jiarui;NIU Qingfei;JIANG Hao;YANG Jiazhong;Pilot decision-making during high-risk flight phases is crucial for aviation safety. While previous studies have indicated that decision-making is influenced by both economic and social reward-punishment mechanisms, their differential effects under varying levels of risk remain unclear. This study aims to investigate the impact of economic and social reward-punishment mechanisms on pilots' decision-making across different risk levels, as well as to explore the underlying cognitive processes utilizing the Hierarchical Drift Diffusion Model(HDDM). A 2(reward-punishment mechanism: economic, social) × 4(risk level: safe, moderate risk, high risk, extremely high risk) factorial experimental design was conducted with 32 licensed commercial pilots(flight hours=230-260). Participants completed a simplified landing task in which they had to decide whether to continue or terminate the landing based on four risk levels(safe, moderate risk, high risk, and extremely high risk) under both economic and social reward-punishment mechanisms. Landing rates, response times, and HDDM parameters were recorded for each participant. Statistical analyses included repeated measures ANOVA, K-means clustering, and HDDM modeling using Markov-chain Monte Carlo sampling(5 000 samples, ■< 1.1, effective sample size > 400). The results indicated that:(1) The social reward-punishment mechanism significantly decreased pilots' landing rates under moderate(p<0.001) and high-risk conditions(p<0.001), and it shortened decision response times under high-risk(p=0.029) and extremely high-risk conditions(p<0.001) compared to the economic reward-punishment mechanism.(2) Drift rate analysis indicated that the social reward-punishment mechanism enhanced information accumulation rates under high-risk and extremely high-risk conditions, thereby accelerating decision-making.(3) Starting point analysis revealed that pilots operating under the social reward-punishment mechanism displayed a preference for landing in safe conditions but were more inclined to opt for go-around decisions in extremely high-risk situations. These findings suggest that the social reward-punishment mechanism can effectively steer pilots towards safer decision-making, particularly in uncertain and high-risk situations. The HDDM analysis offers deeper insights into cognitive mechanisms, including information accumulation rates and shifts in decision preferences. This study provides theoretical implications for optimizing reward-punishment systems in aviation, ultimately aimed at enhancing flight safety.
Quantitative evaluation model for flight safety style: a research study using combined weighting algorithms
WANG Lei;WEI Zixin;SUN Chuanhan;To tackle the challenges associated with the quantitative evaluation of pilots' flight safety styles and enhance its management, we developed a combined-weighting quantitative evaluation model that integrates both attitude and behavioral dimensions. Initially, we established an evaluation index system encompassing two key dimensions: flight safety attitudes(including self-assessment and peer assessment) and flight safety behaviors(comprising operational behavior evaluations and non-operational behavior peer assessments). Specifically, the self-assessment and peer assessment of flight safety attitudes, along with non-operational behavior peer assessments, were conducted using scale-based evaluations. In contrast, operational behavior evaluation was performed based on the classification theory of unsafe behaviors, supplemented by Flight Operational Quality Assurance(FOQA) data. Additionally, a composite weighting approach was employed, integrating the Analytic Hierarchy Process(AHP), Criteria Importance Through Intercriteria Correlation(CRITIC), and Coefficient of Variation(CV) methods to develop a weighted Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS) model. Subsequently, twenty-seven pilots completed questionnaires assessing the importance of flight safety style evaluation indicators, with subjective weights calculated using the AHP. Data collection involved:(1) self-assessment data and FOQA records from twenty pilot subjects; and(2) annual FOQA records from their respective fleets. Peer assessments of flight safety attitudes and non-operational behaviors were conducted by twenty-nine incumbent airline pilots who had previously flown with these subjects. Quantitative scores for the flight safety styles of all twenty subjects were calculated using both the AHP-CRITIC-TOPSIS and AHP-CV-TOPSIS models. The validity of the model was verified through:(a) positive correlation analysis between comprehensive scores and subjective flight safety style ratings;(b) negative correlation analysis with the incidence rates of serious procedural deviations; and(c) comparative distribution analysis of quantitative scores derived from both models. The results indicate that both the AHP-CRITIC-TOPSIS and AHP-CV-TOPSIS quantitative evaluation models for flight safety style are effective assessment tools. Notably, the AHP-CRITIC-TOPSIS model is deemed more effective and applicable for evaluating flight safety styles due to its higher accuracy, stronger correlation with actual flight safety performance, and more stable evaluation results.
Effects of chronic stress on job satisfaction among regional airline pilots: exploring the moderating role of psychological resilience
WANG Wenchao;GU Xinyu;This study investigated the impact of chronic stress on job satisfaction among 83 regional airline pilots in China, with a particular focus on the moderating role of psychological resilience. A stratified random sampling design was employed based on flight rank(e.g., instructor pilot, pilot, copilot, flying cadet). Chronic stress, psychological resilience, and job satisfaction were assessed using validated scales: TICS-E(Cronbach's α=0.952), CD-RISC(Cronbach's α=0.832), and MSQ(Cronbach's α=0.906). Data analysis utilized Structural Equation Modeling(SEM) for confirmatory factor analysis, confirming good convergent validity(CR values ranging from 0.856 to 0.913, AVE values between 0.592 and 0.687), as well as path analysis and hierarchical regression analysis to test for moderation. The model fit indices were found to be acceptable(χ2/ν=2.637, RMSEA=0.072, CFI=0.928, TLI=0.914). The results revealed a significant negative direct effect of chronic stress on job satisfaction, with a standardized path coefficient of-0.448(p<0.01), thus supporting Hypothesis H1. Notably, psychological resilience exhibited a significant positive moderating effect on this relationship, indicated by the interaction term of 0.213(p<0.01), thereby confirming Hypothesis H2. Johnson-Neyman analysis demonstrated that when psychological resilience scores exceeded 3.25+0.5(M+SD), the detrimental impact of chronic stress on job satisfaction was mitigated by 37%. Hierarchical regression further reinforced this moderation effect, with an interaction term coefficient of 0.346, and the findings remained consistent after controlling for other variables. Violin plots illustrated that pilots with lower job satisfaction experienced higher levels of chronic stress. Correlation analysis indicated a negative association between chronic stress and both psychological resilience(r=-0.245, p<0.05) and job satisfaction(r=-0.529, p<0.01). Conversely, psychological resilience showed a positive correlation with job satisfaction(r=0.316, p<0.01). These results suggest that psychological resilience acts as a buffer against the negative impact of chronic stress on job satisfaction by enhancing emotional regulation and self-efficacy, thereby serving as a psychological barrier to stress-related safety risks.
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