This paper presents a modified topological vulnerability analysis for the metro network. The flow impacts and the capacity constraint are taken into account in this work. A node-based congestion is considered in the travel time model for the metro network. The logit-based path choice model is adopted to assign the flow in the network. Based on the present approach, the efficiencies of the metro network with either fully or partly damaged stations can be analyzed. The vulnerability of the Beijing metro network was studied based on the present method. The results show that the flow in the network plays an important role in the vulnerability analysis. Topological methods neglecting either the flow or the flow impacts would underestimate the vulnerability of the network. Both the magnitudes and patterns of origin-destination (O-D) demand influence on the assessment of vulnerabilities of the metro network and on the importance ranking of stations. Additionally, in a busy metro network the vulnerability...

Due to the influence on the sectors and industries that have strong relationships with the air transportation industry, flight delays have a huge impact on China’s economy. In this study, the input–output method and the Ghosh model were used to analyze the indirect economic impacts of flight delays on China’s economy. We also constructed simultaneous demand-fare equations to estimate the direct output of the air industry as a result of flight delay control. The final results showed that the total indirect impact of flight delays on China was Ұ350.71 billion in 2013, stressing the importance of controlling flight delays. Since delay control not only provides direct benefits to passengers and carriers, but also indirectly contributes to regional economies, the efforts benefit the entire Chinese society. The great investment in flight delay governance should be shared among various producing sectors.

Traffic flow prediction is a significant component for the new generation intelligent transportation. In the field of air transportation, accurate prediction of airport flight flow can help airlines schedule flights and provide a decision-making basis for airport resource allocation. With the help of Deep Learning technology, this paper focuses on the characteristics of flight flow easily disturbed by environmental factors, studies the spatiotemporal dependence between flight flows, and predicts the spatiotemporal distribution of flight flows from the airport network level. We proposed a deep learning architecture named ATFSTNP, which combining the residual neural network (ResNet), graph convolutional network (GCN), and long short-term memory (LSTM). Based big data analytics of air traffic management, this method takes the spatiotemporal causal relationship between weather impact and flight flow as the core, and deeply mines the complex spatiotemporal relationship of flight flow. The model’s methodologies are improved from the practical application level, and extensive experiments conducted on the China’s flight operation dataset. The results illustrate that the improved model has significant advantages in predicting the flight flow under weather affect. Even in the complex and variable external environment, the model can still accurately predict the spatiotemporal distribution of the airport network flight flow, with strong robustness.

The Boeing 737 MAX crisis and COVID-19 pandemic have seriously influenced the development of China’s aircraft leasing industry in the past two years. This paper applies system dynamics theory to explore the sustainable development of China’s aircraft leasing industry. It analyses the dynamic mechanism and constructs a system dynamics model. Based on China’s macroeconomic data and historical data from the financial, aviation, and leasing industries, it aims to stimulate the development of China’s aircraft leasing industry in the next five years. Through sensitivity analysis, this research finds that changes in GDP growth have the most obvious impact on the sustainable development of China’s aircraft leasing industry. Reducing the average financing cost and the income tax rate of aircraft leasing companies, increasing their investment in talent, and controlling risk will increase the market share of China’s aircraft leasing companies and promote the development of the industry. However, increasing the number of aircraft leasing companies has little effect on market share. On this basis, this paper proposes policy recommendations to promote the sustainable development of China’s aircraft leasing industry.

Machine learning has influenced the modern transportation in many aspects, such as it could help construct ‘smart airports’ along with big data. Many researchers have discussed a lot of relevant topics in this area. This paper gives a relevant short review, indicating that there are mainly 4 issues which are under the concept of machine learning in the construction of smart airports in recent times. The issues are flight delays, airport environment, operation management of airport, safety of airport. The researches of the 4 issues have become the mainstream for the studies on smart airports.