The Key Technique And Algorithms In Atfm Based On Cdm Theory

In recent years, with the traditional air traffic (ATFM) program, which is based on Central decision making, cannot meet the growing air traffic flow any longer. The collaborative decision making (CDM) theory was introduced into the air traffic flow management system in 90's in the last century, which is based on distributed decision making. In Europe and the United States and some other countries, the CDM-based air traffic management system has been widely used and has achieved remarkable results. Collaborative ATFM has become a hot area of research.In this paper, a comprehensive analysis of the history and technology development of air traffic flow management system was given, as well as the comprehensive study of the latest research results at abroad and inland. And then, discuss on of the key issues of the latest research in air traffic flow management. First of all, in-depth study of collaborative decision making theory and its major applications in the air traffic flow managemen, including its operating mechanism, the way of the resources allocation. Secondly, study of the key techniques of the Collaborative ATFM, including the ground delay programs enhancement strategy, the collaborative airport surface management strategies, the collaborative route strategy and the key technologies and algorithms thereinto. On this basis, give the improvement the collaborative ATFM slot allocation algorithm research based on the fairness and effectiveness, and give forecast trajectory optimization model for airport surface management system, designed to accelerate the development of collaborative ATFM, and introduce collaborative decision making theory to China's air traffic flow management system.In this paper, the main work and research results are as follows:1. The establishment of arrival and departure capacity model, as well as collaborative arrival and departure capacity optimization model. According to the demand of the arrival and departure request, compute the arrival and departure capacity by dynamic programming method to match the demand, and gives the collaborative algorithms in arrival and departure slot allocation. The simulation results testify that the collaborative arrival and departure capacity optimization model is able to increase available slot resources to arrival and departure slot allocation in a large extent, and enable the arrival and departure capacity match with demand, thereby greatly reducing the delay in congestion periods.2. Study of initial slot allocation algorithm in collaborative ATFM, which is RBS algorithm, RBD algorithm and E-RBD algorithm. Based on the principle of equity, raise the substitute model for RBS algorithem, consider the fairness for the airlines, the airlines'passengers and the optimization of traffic in three areas, respectively establish EAD model to minimize the average delays fo airlines, establish EPD model to minimize the delay for the passengers on a certain flight, as well as establish OFM model to minimize the total delays for the flights which join into the GDP-E. Compute the data in actual flight schedules, and finally consider the three objectives, a comprehensive solution of multi-objective analysis of simulation results is given, the results show that the established models can achieve the desired effect, so that the interests of all parties can be guaranteed.3. Study of redistribution slot allocation algorithm in collaborative ATFM, which is Compression algorithm. Giving attentions to fairness principle, we consider of the "effectiveness" principle in mind, and put forward the optimization algorithm, which is Greed-Compression algorithm, the whole process of algorithm is given. We draw a simulation by a Java programm. By the analysis of the simulation results of the algorithm, it can be seen that the operating efficiency of the the ground delay program has been enhanced, it ensure the fairness both with effectiveness of the slot allocation.4. The establishment of a collaborative taxiway trajectory prediction model, and give the conflict detection functions. Optimize the taxiway prediction trajectory under the conditions of ensuring the safety of aircraft on airport surface. Compute the above model by event-triggered based A * algorithm and the overall model of Floyd algorithm. Analyze and compare the two algorithms and the computation process. The results show that the event-triggered based A* algorithm has advantages, such as high efficiency and agile, while the traffic is not so congested, Otherwise, the overall model of Floyd algorithm is more superior than the event-triggered based A* algorithm, it can get the overall optimal results. Finally, we propose to combine the two algorithms.5. Through a summary of research in this paper, we prospect to the further study direction on Collaborative ATFM.