Under the background of economic globalization, the air transport industry developed rapidly. It turns out that the city-to-city network has not been able to adapt well to the development of the society, and the hub-and-spoke network came into being. The hub-and-spoke network demonstrates the advantages of reducing the operating costs of airlines to keep a competitive advantage, and by maintaining the interests of airlines in the rapidly developing context. However, during the operation of aircrafts, they consume fuel and spew a great deal of harmful pollutants into the air, which has an adverse impact on the living environment. This paper explores the impact and external costs associated with hub-and-spoke network in air transport from an environmental perspective. With some mathematical models, we construct a hub-and-spoke network and take a quantitative study on the environmental impact of air transport. For calculating pollutant emissions, meteorological conditions were considered to revise the pollutant emission factors of the Engine Emissions Data Base (EEDB) published by International Civil Aviation Organization (ICAO). The environmental external costs measurement model is employed to calculate the externality of toxic gas and greenhouse gas (GHG). In order to make the study more convincing, two alternative networks are computed: hub-and-spoke network and city-to-city network. It is found that the hub-and-spoke network is associated with poorer environmental impact and environmental external costs because of the different network characteristics and the scale of the fleets. Therefore, under the general trend of green aviation, the environmental impact and environmental external costs associated with hub-and-spoke network in air transport provides a certain reference for airlines’ strategic decision-making.

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.

The green hub‐and‐spoke airline network (GHSAN) is emerging as a dominant feature due to its excellent economic and environmental‐friendly capabilities. However, environmental GHSAN designs still have some concerns, including single pollutant‐domain oversimplification and lack of comprehensive network‐level operation impacts. This paper proposes a multi‐emission‐driven efficient network design approach for GHSAN, utilizing a system, green, and user threefold optimization methodology. The approach includes a multi‐objective optimization model and a two‐layer solving method. The multi‐objective optimization aims at minimizing multiple emissions, including carbon dioxide, carbonic oxide hydrocarbon, and nitric oxide, while also considering transportation system costs and journey user costs. A two‐layer optimization algorithm is adopted to address different scales of optimization. Real‐world results demonstrate that the proposed method mitigates environmental impact and user costs and increases overall airline density in airline networks. The proposed method can have a 16.29% reduction in green‐fold (10 nodes) and a 12.06% decrease in user costs for the user‐fold (10 nodes). As the number of nodes (15, 25, 50 nodes) and hubs (3, 4, 5, 6, 7 hubs) increase, the genetic algorithm (GA) proves to be more efficient and suitable in large‐scale GHSAN. This work is further significant for the long‐term and sustainable development of the future air transport industry.

With the air transport industry developing dramatically, the problem of flight congestion in the terminal area (TMA) has been aggravated. Particularly for arrival aircraft, the extraholding and delay not only increase the workload of air traffic controllers and aircrew but also reduce the operational efficiency. To struggle with this issue, it is crucial to study how to improve the operational efficiency of arrival aircraft. Under the background of the rapid promotion of Point Merge (PM) procedure program in the busy TMAs in the world, this paper firstly combines the optimization of PM procedure and arrival aircraft sequencing, aiming to maximize the operational benefits of arrival aircraft in TMA. Firstly, the framework of the PM procedure is set up to replace the traditional approach route known as Standard Instrument Arrival (STAR). After that, the PM procedure optimization model is established to provide a better approach route for each arrival aircraft. Then for a flow of arrival traffic, an aircraft sequencing model is established to readjust the landing sequence of arrival aircraft, thus further improving the operational benefits of arrival aircraft. Finally, taking the TMA of Shanghai Hongqiao International Airport as the case, we compare the impact of different flight procedures and different landing sequences on the operational benefits of arrival aircraft. The experimental results show that the application of the PM procedure optimization model and sequencing model can effectively improve the operational benefits on the premise of ensuring safety, thus realizing the safe and orderly approach in TMA.