Search Results (3)

Despite advanced aviation safety systems, recurring operational failures demonstrate that current safety management system (SMS) implementation practices remain predominantly reactive, with organizations adopting SMS frameworks theoretically embracing Safety-II philosophy while continuing Safety-I-oriented reactive management. This study develops an integrated framework for implementing both Safety-I and Safety-II principles in aviation safety management, addressing the gap between SMS theoretical requirements and actual implementation. Using the HEAR (human error analysis and reduction) framework, we analyzed three representative aviation cases involving FMS operation, turbulence response, and aircraft energy management through a qualitative multiple-case study design. Data collection utilized internal safety reports, official investigation reports, and reconstructed operational scenarios. The analysis employed a four-phase approach integrating predetermined categorization with inductive pattern recognition. Results revealed that 87% of all causes were organizational factors—6.7 times higher than individual/task factors (13%)—yet safety management responses primarily target individual behaviors. We defined “flight crew’s resilient behavior” and developed implementation guidelines by integrating the HEAR framework with the LPAC (learn, plan, adapt, coordinate) model and PAM (pressures, adaptations, and manifestations) framework. Effectiveness evaluation demonstrated a transition from 54 discrete contributing factors to 19 systematically related factors with clearer implementation pathways. Our integrated framework enables organizations to systematically implement both Safety-I analytical capabilities and Safety-II adaptive responses, transforming safety management from reactive “failure prevention” to proactive “success expansion”. Full article

The complex layout of the airport surface, coupled with interrelated vehicle behaviors and densely mixed traffic flows, frequently leads to operational conflict risks. To address this issue, research was conducted on the recognition of characteristics and risk assessment for airport surface operations in mixed traffic flows. Firstly, a surface topological network model was established based on the analysis of the physical structure features of the airport surface. Based on the Monte Carlo simulation method, the simulation framework for airport surface traffic operations was proposed, enabling the simulation of mixed traffic flows involving aircraft and vehicles. Secondly, from various perspectives, including topological structural characteristics, network vulnerabilities, and traffic complexity, a comprehensive system for feature indices and their measurement methods was developed to identify risk hotspots in mixed traffic flows on the airport surface, which facilitated the extraction of comprehensive risk elements for any node’s operation. Finally, a weighting rule for risk hotspot feature indices based on the CRITIC–entropy method was designed, and a risk assessment method for surface operations based on TOPSIS–gray relational analysis was proposed. This method accurately measured risk indices for airport surface operations hotspots. Simulations conducted at Shenzhen Bao’an International Airport demonstrate that the proposed methods achieve high simulation accuracy. The identified surface risk hotspots closely matched actual conflict areas, resulting in a 20% improvement in the accuracy of direct risk hotspot identification compared to simulation experiments. Additionally, 10.9% of nodes in the airport surface network were identified as risk hotspots, including 3 nodes with potential conflicts between aircraft and ground vehicles and 21 nodes with potential conflicts between aircraft. The proposed methods can effectively provide guidance for identifying potential “aircraft–vehicle” conflicts in complex airport surface layouts and scientifically support informed decisions in airport surface operation safety management. Full article

Moving traces are essential data for target detection and associated behavior recognition. Previous studies have used time–location sequences, route maps, or tracking videos to establish mathematical recognition models for behavior recognition. The multimodal approach has seldom been considered because of the limited modality of sensing data. With the rapid development of natural language processing and computer vision, the multimodal model has become a possible choice to process multisource data. In this study, we have proposed a mathematical model for aircraft behavior recognition with joint data manners. The feature abstraction, cross-modal fusion, and classification layers are included in the proposed model for obtaining multiscale features and analyzing multimanner information. Attention has been placed on providing self- and cross-relation assessments on the spatiotemporal and geographic data related to a moving object. We have adopted both a feedforward network and a softmax function to form the classifier. Moreover, we have enabled a modality-increasing phase, combining longitude and latitude sequences with related geographic maps to avoid monotonous data. We have collected an aircraft trajectory dataset of longitude and latitude sequences for experimental validation. We have demonstrated the excellent behavior recognition performance of the proposed model joint with the modality-increasing phase. As a result, our proposed methodology reached the highest accuracy of 95.8% among all the adopted methods, demonstrating the effectiveness and feasibility of trajectory-based behavior recognition. Full article