Runway incursions (RIs) have emerged as a major threat to airport surface safety, driven by the coupled influence of human, equipment, environmental, and management factors. Conventional assessment methods struggle to simultaneously capture the fuzziness of expert linguistic judgment and the randomness of operational conditions. This study proposes an integrated DEMATEL–Cloud–TOPSIS framework for runway incursion risk assessment and validates it at Chengdu Tianfu International Airport. A hierarchical indicator system comprising 24 indicators across four dimensions—Human (H), Equipment (M), Environment (E), and Management (G)—was constructed from 90 RI cases collected between 2018 and 2023. DEMATEL quantified inter-indicator causal dependencies and DEMATEL-derived weights; the Cloud model translated linguistic expert judgments into digital characteristics (Ex, En, He); and TOPSIS produced relative closeness coefficients for risk ranking. Human, equipment, and environmental risks are all at a medium-risk, while management risk is at a low-risk, but significant differences still exist. Management achieved the highest closeness ($C_i$ = 0.6322) and Environment the lowest ($C_i$ = 0.5096). At the indicator level, ATC Instruction Accuracy (H1) exhibited the greatest operational maturity ($C_i$ = 0.9119), whereas Unclear Crew Coordination (H6) showed the lowest relative closeness ($C_i$ = 0.0156), followed by Aircraft Equipment (M5) ($C_i$ = 0.0195). Meanwhile, Runway Configuration Complexity (E2) remained a weak structural factor within the Environmental dimension ($C_i$ = 0.1502). The framework provides an interpretable, quantitative basis for targeted safety management at complex hub airports.

Runway incursions remain a major threat to airport surface safety, and effective prevention depends on the accurate identification of causal risk factors and their interaction mechanisms. However, existing studies have mainly focused on isolated risk factors or static causal relationships, offering limited insight into the hierarchical structure and dynamic propagation of runway incursion risk in complex operational environments. To address this gap, this study proposes a quantitative framework for runway incursion risk analysis by integrating grounded theory and complex network theory. Published runway incursion cases in the Chinese civil aviation system from 2022 to 2025 were systematically analyzed through open coding, axial coding, and selective coding, resulting in a hierarchical indicator system comprising five main categories, twelve subcategories, and 112 risk indicators. Based on this system, a runway incursion causal network was constructed to characterize the causal interdependencies among risk factors. Node importance was evaluated using a TOPSIS-based multi-criteria method integrating multiple network metrics, and a load-distribution-based propagation mechanism was introduced to quantify the risk propagation probability and risk propagation intensity of each node. The results indicate that insufficient night lighting (N99), taxi-route memory errors (N14), ambiguous controller instructions (N1), and excessive controller workload (N10) exhibit relatively high risk propagation probability and risk propagation intensity, indicating their critical roles in the evolution and cascading propagation of runway incursion risk. These findings demonstrate that the proposed framework can effectively capture both the structural importance and propagation characteristics of causal risk factors. Therefore, this study provides quantitative support for understanding runway incursion risk evolution and for developing targeted prevention strategies and post-incident response measures to improve runway safety management.

Aircraft wake vortices pose significant threats to following aircraft during takeoff and landing phases. Coherent Doppler lidar provides an effective remote sensing technique for wake vortex monitoring through radial velocity measurements. However, reliable identification of wake vortices from lidar observations remains challenging due to noise and the complex multi-scale evolution of vortex structures. In this study, we propose a physics-guided multi-scale deep network (HMNet) for aircraft wake vortex identification. First, we propose a denoising module (DE) to suppress noise in radial velocity fields. Subsequently, we design a hybrid multi-scale backbone network containing a hybrid multi-scale feature extraction module (HMFE) to capture vortex structures at different spatial scales. Furthermore, we propose a feature gradient guidance module (FGGM) to incorporate physically meaningful gradient cues and enhance vortex-sensitive features. HMNet is evaluated and tested on 1401 radial velocity field data samples collected on the runway at Shenzhen Bao’an Airport. The experimental results show that HMNet achieves 97.15% accuracy, 95.83% recall, and 96.84% F1 score. Compared with the baseline VGG16 and Random Forest, HMNet improves accuracy by 6.18% and 11.88%, respectively. These results demonstrate that HMNet provides an effective solution for lidar-based wake vortex identification and can support the development of intelligent air traffic management.