This paper takes the analytic hierarchy process as the basic method to establish the air traffic control tower safety risk factor set. In combination with the gray theory and fuzzy mathematics principle to determine the gray fuzzy evaluation conclusion set and the index layer factor score value matrix, we calculate the gray evaluation coefficient and build the evaluation weight matrix to evaluate the control operation risk of air traffic control towers. Finally, the case verification is carried out, and the risk assessment value of the air traffic control tower used in the case is calculated as 5.6325, which belongs to the “general risk” level. The evaluation method adopted in this paper can provide reasonable suggestions for operational risk management of air traffic control towers.
With the increase in air transportation, the problem of of the airport gate shortage has become increasingly prominent. Unreasonable gate allocation will affect resource utilization, reduce customer satisfaction, and even lead to chaos in airport operations, restricting the rapid development of the airport. As the expansion of the gate requires a lot of capital, manpower and material resources, it is extremely important to optimize the configuration of the existing gates using reasonable and efficient methods. This article reviews the research status of the optimization of airport gates at home and abroad. The three main research methods are summarized and the general process of airport gate optimization configuration is sorted out, and the commonly used airport gate optimization configuration models are reviewed, including the main decision variables, optimization goals and constraints. The paper briefly analyzes the solution of the model based on the accurate algorithms, heuristic algorithm and meta-heuristic algorithm, and looks forward to the future research direction of the optimal configuration of airport gates.
In order to meet the needs of collaborative decision making (CDM), considering the demands of air traffic control units, airlines, airports and passengers, the many-objective optimization scheduling problem of arrival flights is studied systematically. Through in-depth analysis of the operation characteristics of arrival flights, multiple optimization objectives of arrival flight scheduling are constructed according to the demands of all parties. Considering the constraints of air traffic control separation, landing time window and constrained position shifting (CPS), a many-objective optimization scheduling model of arrival flights is established; The reference vector guided evolutionary algorithm (RVEA) is used to find the Pareto optimal solution. The simulation results show that compared with first come first served (FCFS) method, the delay fairness is improved by 56.7%, the total runway occupancy time is reduced by 15% and the weighted total delay is reduced by 25.1%.
Nowadays, with the rapid development of civil aviation in China, the volume of air passenger transportation is on a continuous growth. As an important place to provide ground services for passengers, the terminal is becoming saturated with capacity, which will not only cause more flight delays and reduce the efficiency of arrival and departure operations, but also bring negative impact to the normal operation and economic benefits of the airport. Taking Shanghai Hongqiao International Airport as an example, this paper establishes an Anylogic simulation model by encapsulating agent, evaluates and analyzes the passenger capacity of terminal critical resources, the weaknesses of the departure process and the bottlenecks of terminal operations under pressurized conditions, and makes reasonable suggestions for the terminal resources allocation and the optimization of related management aspects.
With the rapid growth of air traffic flow, in order to accurately analyze the spatial distribution characteristics of air traffic flow in the terminal area, optimize the design of arrival and departure route and improve the service level of air traffic control, a method for identifying the main air traffic flow from a large number of arrival aircraft flight trajectories is studied. Based on the analysis of the characteristics of the actual operating trajectory data, the flight characteristics are retained by the resampling method, the trajectory feature points are extracted, and the K-means algorithm is used to cluster the flight trajectories in the terminal area to achieve the identification of the prevalent air traffic flow, and the difference between the central trajectories of aircraft flight during different time periods of daytime and nighttime is compared and analyzed. The results of the experimental research show that the method can effectively identify the prevalent air traffic flow from the complicated flight trajectory information through an instance verification of Guangzhou Baiyun Airport.
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