为了确定低空空域内航空器飞行的安全间隔和风险概率,基于国际民航组织标准和我国民航局规定,根据航空器动力学原理,采用看见避让(see and avoid)原则,在飞行规则、能见度要求、反应时间、航空器速度以及盘旋坡度角或航空器爬升角度等...为了确定低空空域内航空器飞行的安全间隔和风险概率,基于国际民航组织标准和我国民航局规定,根据航空器动力学原理,采用看见避让(see and avoid)原则,在飞行规则、能见度要求、反应时间、航空器速度以及盘旋坡度角或航空器爬升角度等约束条件下,建立了同高度对头飞行冲突和交叉飞行冲突的冲突避让轨迹数学模型,并根据HCR(human cognitive reliability)理论建立了飞行员反应失效概率模型.数值分析结果表明,低空空域航空器同高度对头相遇存在一定的违反安全间隔的风险概率,而同高度交叉相遇飞行的航空器能安全解脱冲突.展开更多
Conflict avoidance （CA） plays a crucial role in guaranteeing the airspace safety. The cur- rent approaches, mostly focusing on a short-term situation which eliminates conflicts via local adjust- ment, cannot provide...Conflict avoidance （CA） plays a crucial role in guaranteeing the airspace safety. The cur- rent approaches, mostly focusing on a short-term situation which eliminates conflicts via local adjust- ment, cannot provide a global solution. Recently, long-term conflict avoidance approaches, which are proposed to provide solutions via strategically planning traffic flow from a global view, have attracted more attentions. With consideration of the situation in China, there are thousands of flights per day and the air route network is large and complex, which makes the long-term problem to be a large-scale combinatorial optimization problem with complex constraints. To minimize the risk of premature convergence being faced by current approaches and obtain higher quality solutions, in this work, we present an effective strategic framework based on a memetic algorithm （MA）, which can markedly improve search capability via a combination of population-based global search and local improve- ments made by individuals. In addition, a specially designed local search operator and an adaptive local search frequency strategy are proposed to improve the solution quality. Furthermore, a fast genetic algorithm （GA） is presented as the global optimization method. Empirical studies using real traffic data of the Chinese air route network and daily flight plans show that our approach outper- formed the existing approaches including the GA .based approach and the cooperative coevolution based approach as well as some well-known memetic algorithm based approaches.展开更多
This paper reviews existing approaches to the airport gate assignment problem （AGAP） and presents an optimization model for the problem considering operational safety constraints. The main objective is to minimize t...This paper reviews existing approaches to the airport gate assignment problem （AGAP） and presents an optimization model for the problem considering operational safety constraints. The main objective is to minimize the dispersion of gate idle time periods （to get robust optimization） while ensuring appropriate matching between the size of each aircraft and its assigned gate type and avoiding the potential hazard caused by gate apron operational conflict. Genetic algorithm is adopted to solve the problem, An illustrative example is given to show the effectiveness and efficiency of the algorithm. The algorithm performance is further demonstrated using data of a terminal from Beijing Capital International Airport （PEK）.展开更多
文摘为了确定低空空域内航空器飞行的安全间隔和风险概率,基于国际民航组织标准和我国民航局规定,根据航空器动力学原理,采用看见避让(see and avoid)原则,在飞行规则、能见度要求、反应时间、航空器速度以及盘旋坡度角或航空器爬升角度等约束条件下,建立了同高度对头飞行冲突和交叉飞行冲突的冲突避让轨迹数学模型,并根据HCR(human cognitive reliability)理论建立了飞行员反应失效概率模型.数值分析结果表明,低空空域航空器同高度对头相遇存在一定的违反安全间隔的风险概率,而同高度交叉相遇飞行的航空器能安全解脱冲突.
基金co-supported by the National High-tech Research and Development Program of China (Grant No.2011AA110101)the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (Grant No. 60921001)China Scholarship Council
文摘Conflict avoidance （CA） plays a crucial role in guaranteeing the airspace safety. The cur- rent approaches, mostly focusing on a short-term situation which eliminates conflicts via local adjust- ment, cannot provide a global solution. Recently, long-term conflict avoidance approaches, which are proposed to provide solutions via strategically planning traffic flow from a global view, have attracted more attentions. With consideration of the situation in China, there are thousands of flights per day and the air route network is large and complex, which makes the long-term problem to be a large-scale combinatorial optimization problem with complex constraints. To minimize the risk of premature convergence being faced by current approaches and obtain higher quality solutions, in this work, we present an effective strategic framework based on a memetic algorithm （MA）, which can markedly improve search capability via a combination of population-based global search and local improve- ments made by individuals. In addition, a specially designed local search operator and an adaptive local search frequency strategy are proposed to improve the solution quality. Furthermore, a fast genetic algorithm （GA） is presented as the global optimization method. Empirical studies using real traffic data of the Chinese air route network and daily flight plans show that our approach outper- formed the existing approaches including the GA .based approach and the cooperative coevolution based approach as well as some well-known memetic algorithm based approaches.
文摘This paper reviews existing approaches to the airport gate assignment problem （AGAP） and presents an optimization model for the problem considering operational safety constraints. The main objective is to minimize the dispersion of gate idle time periods （to get robust optimization） while ensuring appropriate matching between the size of each aircraft and its assigned gate type and avoiding the potential hazard caused by gate apron operational conflict. Genetic algorithm is adopted to solve the problem, An illustrative example is given to show the effectiveness and efficiency of the algorithm. The algorithm performance is further demonstrated using data of a terminal from Beijing Capital International Airport （PEK）.
