摘要
为了解决对流云团状态不确定条件下的改航路径规划问题,利用云团预测状态的不确定性测度和外推位置随机误差建立多雷暴云团的状态转移矩阵,用于预测改航路径规划网络的时变阻滞状态.以期望改航代价最小为优化目标,以航段连续性和最大穿越风险代价为约束条件,建立了动态改航路径规划模型.针对确定性和不确定性两种天气场景的16种初始状态,采用遗传算法对模型进行求解,结果表明,通过预测云团状态的不确定变化,改航路径能策略性地选择穿越初始状态不可航云团或绕飞初始状态可航云团.与确定性改航策略相比,绕飞距离减少了40%,穿越云团次数减少了30.8%,改航路径的安全性和经济性均得到有效改善.
In order to solve the reroute planning problem with uncertain severe convective weather, a state transition matrix was built using the uncertain measure of forecasting state and the random error of extrapolating position for rainstorm cloud cluster. The matrix was then used to predict the time-varying delay conditions of the reroute planning network. On this basis, a dynamic reroute planning model, with the least expected rerouting cost as its optimized objective, was developed under the constraints of path continuity and maximum penetrating risk, and solved by the generic algorithm subsequently. Finally, two kinds of weather scenarios with 16 initial states of cloud cluster were set up in a case study to verify the proposed model. Simulation results show that the rerouting paths will strategically penetrate clouds with unsafe initial state or detour clouds with safe initial state, by predicting the uncertain changes of cloud cluster states in uncertain weather scenarios. In addition, with the dynamic rerouting strategy under an uncertain scenario, the detouring distance decreases by 40% and the penetrating count decreases by 30. 8%, compared with those under a certain weather scenario. Therefore, both safety and economy of rerouting oaths are imoroved effectivelv.
出处
《西南交通大学学报》
EI
CSCD
北大核心
2012年第4期686-691,共6页
Journal of Southwest Jiaotong University
基金
国家自然科学基金资助项目(60972006
61039001)
中央高校基本科研业务费专项资金资助项目(ZXH2011D010)
关键词
空中交通流量管理
动态改航规划
不确定性对流天气
遗传算法
air traffic flow management
dynamic reroute planning
uncertain convective weather
generic algorithm
