Dynamic Causal Weighting-Based Risk Propagation Modeling for Airport Movement Areas

To analyze how dynamic operational conditions (e.g., environment changes, traffic surges) drive risk propagation in airport movement areas, this study proposes a data-driven framework integrating complex network theory and reinforcement learning to decode causal relationships from historical incidents. We construct a risk propagation network and leverage causal convolutional reinforcement learning (CCRL) to dynamically quantify inter-node causal strength through temporal pattern mining. First, an enhanced grey relational model (with standardized discrimination coefficients) identifies 63 critical risk factors from multi-source data, addressing extremum-induced distortion in traditional methods. Second, a capacity-load propagation framework classifies nodes into impedance/cumulative types for heterogeneous risk modeling. Third, the CCRL framework facilitates dynamic adjustment of propagation weights through continuous updates of time-varying causal strengths. Experimental results show that our model outperforms Dynamic Bayesian Networks (DBN) by 5% in prediction accuracy under data scarcity (n = 1,067 incidents) under limited data samples and strong time-varying conditions. The model objectively identifies 63 critical risk factors and enables targeted control strategies that reduce risk diffusion indices by 28%, providing a novel and effective approach for deconstructing time-varying risk propagation in airport movement areas.