Airspace safety and airport capacity are two key challenges to sustain the growth in Air Transportation. In this paper, we model the Air Transportation Network as two sub-networks of airspace and airports, such that t...Airspace safety and airport capacity are two key challenges to sustain the growth in Air Transportation. In this paper, we model the Air Transportation Network as two sub-networks of airspace and airports, such that the safety and capacity of the overall Air Transportation network emerge from the interaction between the two. We propose a safety-capacity trade-off approach,using a computational framework, where the two networks can inter-act and the trade-off between capacity and safety in an Air Transport Network can be established. The framework comprise of an evolutionary computation based air traffic scenario generation using a flow capacity estimation module(for capacity), Collision risk estimation module(for safety) and an air traffic simulation module(for evaluation). The proposed methodology to evolve air traffic scenarios such that it minimizes collision risk for given capacity estimation was tested on two different air transport network topologies(random and small-world) with the same number of airports. Experimental results indicate that though airspace collision risk increases almost linearly with the increasing flow(flow intensity) in the corresponding airport network, the critical flow depend on the underlying network configuration. It was also found that, in general, the capacity upper bound depends not only on the connectivity among airports and their individual performances but also the configuration of waypoints and mid-air interactions among conflicts. Results also show that airport network can accommodate more traffic in terms of capacity but the corresponding airspace network cannot accommodate the resulting traffic flow due to the bounds on collision risk.展开更多
Describing spatial safety status is crucial for high-density air traffic involving multiple unmanned aerial vehicles （UAVs） in a complex environment. A probabilistic approach is proposed to measure safety situation ...Describing spatial safety status is crucial for high-density air traffic involving multiple unmanned aerial vehicles （UAVs） in a complex environment. A probabilistic approach is proposed to measure safety situation in congested airspace. The occupancy distribution of the airspace is represented with conflict probability between spatial positions and UAV. The concept of a safety envelope related to flight performance and response time is presented first instead of the conventional fixed-size protected zones around aircraft. Consequently, the conflict probability is performance-dependent, and effects of various UAVs on safety can be distinguished. The uncertainty of a UAV future position is explicitly accounted for as Brownian motion. An analytic approximate algorithm for the conflict probability is developed to decrease the computational consumption. The relationship between safety and flight performance are discussed for different response times and prediction intervals. To illustrate the applications of the approach, an experiment of three UAVs in formation flight is performed. In addition, an example of trajectory planning is simulated for one UAV flying over airspace where five UAVs exist. The validation of the approach shows its potential in guaranteeing flight safety in highly dynamic environment.展开更多
文摘Airspace safety and airport capacity are two key challenges to sustain the growth in Air Transportation. In this paper, we model the Air Transportation Network as two sub-networks of airspace and airports, such that the safety and capacity of the overall Air Transportation network emerge from the interaction between the two. We propose a safety-capacity trade-off approach,using a computational framework, where the two networks can inter-act and the trade-off between capacity and safety in an Air Transport Network can be established. The framework comprise of an evolutionary computation based air traffic scenario generation using a flow capacity estimation module(for capacity), Collision risk estimation module(for safety) and an air traffic simulation module(for evaluation). The proposed methodology to evolve air traffic scenarios such that it minimizes collision risk for given capacity estimation was tested on two different air transport network topologies(random and small-world) with the same number of airports. Experimental results indicate that though airspace collision risk increases almost linearly with the increasing flow(flow intensity) in the corresponding airport network, the critical flow depend on the underlying network configuration. It was also found that, in general, the capacity upper bound depends not only on the connectivity among airports and their individual performances but also the configuration of waypoints and mid-air interactions among conflicts. Results also show that airport network can accommodate more traffic in terms of capacity but the corresponding airspace network cannot accommodate the resulting traffic flow due to the bounds on collision risk.
基金supported by the National Basic Research Program of China (No.2011CB707002)
文摘Describing spatial safety status is crucial for high-density air traffic involving multiple unmanned aerial vehicles （UAVs） in a complex environment. A probabilistic approach is proposed to measure safety situation in congested airspace. The occupancy distribution of the airspace is represented with conflict probability between spatial positions and UAV. The concept of a safety envelope related to flight performance and response time is presented first instead of the conventional fixed-size protected zones around aircraft. Consequently, the conflict probability is performance-dependent, and effects of various UAVs on safety can be distinguished. The uncertainty of a UAV future position is explicitly accounted for as Brownian motion. An analytic approximate algorithm for the conflict probability is developed to decrease the computational consumption. The relationship between safety and flight performance are discussed for different response times and prediction intervals. To illustrate the applications of the approach, an experiment of three UAVs in formation flight is performed. In addition, an example of trajectory planning is simulated for one UAV flying over airspace where five UAVs exist. The validation of the approach shows its potential in guaranteeing flight safety in highly dynamic environment.
