Remote tower technology is an important development direction for air traffic control to reduce the construction and operation costs of small or remote airports. However, its digital and virtualized working environment poses new challenges to controllers’ situational awareness (SA). In this study, a dataset is constructed by collecting eye-tracking (ET) and heart rate variability (HRV) data from participants in a remote tower simulation control experiment. At the same time, probe questions are designed that correspond to the SA hierarchy in conjunction with the remote tower control task flow, and the dataset is annotated using the scenario presentation assessment method (SPAM). The annotated dataset containing 25 ET and HRV features is trained using the LightGBM model optimized by a Tree-structured Parzen Estimator, and feature selection and model interpretation are performed using the SHapley Additive exPlanations (SHAP) analysis. The results show that the TPE-LightGBM model exhibits excellent prediction capability, obtaining an RMSE, MAE and adjusted R² of 0.0909, 0.0730 and 0.7845, respectively. This study presents an effective method for assessing and predicting controllers’ SA in remote tower environments. It further provides a theoretical basis for understanding the effect of the physiological state of remote tower controllers on their SA.

Remote tower (rTWR) operations are reshaping air traffic control but introduce significant human-factor risks, notably cognitive fatigue induced by prolonged screen-based visual surveillance. To mitigate these risks in a safety-critical domain where missed detections can be catastrophic, we propose a non-intrusive, multimodal fatigue detection framework fusing ocular and cardiac signals. A high-fidelity simulation study with 36 controllers was conducted to collect eye-tracking and electrocardiogram (ECG) data, from which a 12-dimensional feature vector—integrating gaze entropy and heart rate variability (HRV)—was extracted. Addressing the severe class imbalance and scarcity of fatigue samples in physiological data, we developed a cost-sensitive XGBoost classifier combining SMOTE oversampling with a dynamically weighted loss function. Experimental results show that the proposed framework performed well under mixed-subject evaluation and improved sensitivity to fatigue events. Although a marked performance drop was observed under LOSO evaluation, personalized calibration partially alleviated this limitation, indicating the potential of the framework for real-time fatigue monitoring in remote tower operations.

With the increasing complexity of wildfire emergency response, the aerial emergency response system is imposing increasing demands on both safety and decision rationality of takeoff and landing site selection. Site selection decisions are influenced by multi-dimensional factors, including geographical location, meteorological factors, and operational safety considerations, resulting in a pronounced coupling of multiple factors in the decision-making process. However, existing studies primarily focus on spatial suitability evaluation or technical implementation, often relying on predefined indicator systems and independence assumptions, while lacking a systematic characterization of the influencing factor system and its interrelationships in takeoff and landing site selection. To address this gap, this study proposes a novel structured decision-making framework to systematically analyze and optimize the selection of takeoff and landing sites for bucket firefighting helicopters in wildfire aerial emergency response scenarios. First, a procedural grounded theory approach is employed to systematically identify the influencing factors associated with site selection, thereby constructing a traceable decision-making factor system. Second, fuzzy DEMATEL is applied to model the causal relationships and structural interdependencies among these factors. Finally, a cumulative contribution rate based on centrality is introduced to screen and optimize the decision indicators, resulting in a refined set of key decision indicators. The results reveal the structural roles of different influencing factors in site selection, reduce the reliance on experience-driven judgment, and reconceptualize the problem from traditional indicator weighting and ranking into a structured decision-making process involving multi-factor coupling. This provides systematic decision support for takeoff and landing site selection in wildfire aerial emergency response and establishes a foundation for subsequent spatial suitability analysis and case-based validation. Furthermore, the results are consistent with expert experience and practical operational constraints, indicating the potential applicability of the proposed method in real-world decision-making.

The remote tower operation mode has changed how controllers acquire, integrate, and interpret operational information, making Situation Awareness (SA) prediction more challenging because of the coupling of multiple heterogeneous information sources. To address the limitations of existing physiological-data-based studies in modeling cross-modal relationships and deep multimodal interactions, this study proposes MGRF-Net, a multimodal physiological data-driven model for predicting remote tower controllers’ SA. The model first encodes eye-tracking, electroencephalography, electrocardiography, and electrodermal activity signals independently to obtain high-level temporal representations. A graph attention-enhanced relational learning module is then introduced to capture interactive dependencies among modalities, followed by a dual-branch gated fusion mechanism to adaptively integrate multimodal information and improve prediction stability. Using multimodal physiological data collected from a remote tower simulation experiment and evaluated with 12-fold cross-validation, MGRF-Net achieved 0.0658 RMSE, 0.0461 MAE, 0.8579 R², and 0.9308 PCC, outperforming LightGBM, MLP, PatchTST, iTransformer, and TimeMixer. Ablation experiments and SHAP analysis further confirmed the effectiveness and interpretability of the proposed model. The results indicate that MGRF-Net can effectively capture cross-modal coupling patterns in the formation of controllers’ SA and provides a promising approach for complex cognitive state monitoring and intelligent assistance in remote tower operations.

Wildfires in plateau and mountainous regions are increasingly destructive, often disrupting ground transportation networks and severely constraining emergency logistics, while unmanned aerial vehicles (UAVs) remain limited by payload capacity. To address this challenge, this study proposes an air–ground collaborative emergency material dispatch method for dynamic, time-varying wildfire environments. A multi-layer spatiotemporal network model is developed by incorporating key uncertainties, including fire spread and meteorological fluctuations, into dynamic parameters, and a multi-objective mixed-integer programming framework is established to jointly optimize emergency response time, total dispatch cost, and rescue fairness. To solve the resulting high-dimensional dynamic rescheduling problem, a Fast Ant Colony Optimization-Genetic Algorithm (FACO-GA) integrated with a rolling horizon mechanism is designed. Simulation results under Level 1–10 dynamic perturbations show that, compared with conventional standalone algorithms (GA and ACO), the proposed method demonstrates markedly better robustness and computational efficiency, reducing the extreme average rescheduling response time to 6.80 s, while maintaining a Hypervolume (Hv) retention rate of 96.30% and limiting the Spacing (Sp) change rate to 4.15%. These findings indicate that the proposed approach can effectively overcome computational bottlenecks and provide an adaptive decision-support framework for emergency logistics dispatch in complex wildfire scenarios. Furthermore, comprehensive ablation studies and sensitivity analyses validate the structural necessity of the rolling horizon and ACO modules, ensuring the algorithm’s parameter robustness under extreme stochastic perturbations.