In this paper, sorghum and reed, which possess light stem structures in nature, were selected as biomimetic prototypes. Based on their mechanical stability characteristics—the porous structure at the node feature and the porous feature in the outer skin— biomimetic optimization design, simulation, and experimental research on both the traditional hexagonal structure and a hexagonal honeycomb structure were carried out. According to the two types of straw microcell and chamber structure characteristics, as well as the cellular energy absorption structure for the bionic optimization design, 22 honeycomb structures in 6 categories were considered, including a corrugated cell wall bionic design, a modular cell design, a reinforcement plate structure, and a self-similar structure, as well as a porous cell wall structure and gradient structures of variable wall thickness. Among them, HTPC-3 (a combined honeycomb structure), HSHT (a self-similar honeycomb structure), and HBCT-257 (a radial gradient variable wall thickness honeycomb structure) had the best performance: their energy absorption was 41.06%, 17.84%, and 83.59% higher than that of HHT (the traditional hexagonal honeycomb decoupling unit), respectively. Compared with HHT (a traditional hexagon honeycomb decoupling unit), the specific energy absorption was increased by 39.98%, 17.24%, and 26.61%, respectively. Verification test analysis revealed that the combined honeycomb structure performed the best and that its specific energy absorption was 22.82% higher than that of the traditional hexagonal structure.

To improve the crashworthiness and energy absorption of thin-walled tube structures, a bionic thin-walled tube was designed based on the structural characteristics of antler osteon and the principle of structural bionics and had the same inner and outer diameters and the same gradient thickness as antler osteon. A nonlinear finite element method is used to simulate the crashworthiness of a thin-walled tube with equal gradient thickness variation (EGTTS) under axial and oblique loads. The crashworthiness of EGTTS-7 (Egtts with 7 layers) was evaluated using the complex proportional assessment(COPRAS). A multi-objective particle swarm optimization (MOPSO) algorithm was used to optimize the EGTTS-7 and the Pareto boundary was used to obtain the optimal structure parameters of the EGTTS-7 by using the loading angles of 0°, 10°, 20°, and 30°. It is found that the crashworthiness of the EGTTS is best when the axial load weight factor of the case is large. Compared with EGTTS and circular tubes(CT), F max can be reduced by up to 50.1% and EA can be increased by up to 22.7%.

Traffic network delays seriously affect the air transportation system’s safety, economy, and efficiency, and have always been a global concern. Flight delays usually propagate within airport networks, causing subsequent flights to be delayed. However, existing works lack in considering network causality, and the incorporation of emerging large language models (LLMs). Thus, this paper endeavours to examine the literature on network delay prediction that combines different background knowledge with journal paper publishing data. Particularly, the network delay prediction methods are categorized into four aspects: classic methods without explicit network topology modelling, traditional explicit network-based prediction methods, emerging deep learning methods, and the application of LLMs in transportation. Classic methods without explicit network topology modelling, including statistical analysis, operations research, traditional machine learning and causal inference without network structures, offer interpretable baselines but fail to capture the complexity and nonlinearity of air traffic systems. Traditional explicit network-based prediction methods often approach air traffic systems through frameworks such as complex networks and queuing theory, with an increasing focus on causal relationship analysis. However, these methods fall short in capturing the spatiotemporal dependencies of network delays, particularly in modelling spatiotemporal causality. In contrast, emerging deep learning methods have advanced significantly, enabling the construction of spatiotemporal causal networks and improving the accuracy of network delay prediction. In addition, some future trends are analyzed. It is concluded that graph neural networks with causality and emerging deep learning methods (e.g., spatiotemporal GCN) are identified as essential directions. Moreover, a conceptual AirTraffic LLM is suggested via a novel Spatial-Temporal Causal Large Language Model (STC-LLM) framework for high-precision flight delay prediction, which requires further experimental validation and real-world testing. Nevertheless, issues such as data privacy, model opacity, and high computational costs must be carefully addressed when applying LLMs. Finally, the findings are expected to enhance understanding of delay propagation among researchers, practitioners, and policymakers, while providing insights and guidance to airports, airlines, and air traffic control.