基于GA-BP神经网络的空中交通网络流系统拥堵预测

Congestion prediction of flow system of air traffic network based on GA-BP neural network

从空中交通网络流系统角度出发,将大面积航班延误问题转化为空中交通网络流系统拥堵问题。首先,分析了空中交通网络流系统的容量、流量和流量需求的关系;其次,提取某时段流量需求、累计延误航班变化率、上时段容量变化和某时段容量变化4个预测指标,使用拥堵度作为拥堵评价指标,并基于遗传算法(GA, genetic algorithm)优化BP(back propagation)神经网络建立了拥堵预测模型,对拥堵度进行预测;最后,采用某空中交通网络流系统实际数据进行了算例分析。结果表明,该模型预测效果较好,可为空中交通流量管理部门提供决策依据。

From the perspective of the air traffic network flow system,the large-scale flight delay problem is converted into the congestion problem of the air traffic network flow system,and its congestion prediction is performed.Firstly,the relationship between capacity,flow and flow demand of the air traffic network flow system is analyzed,and the four forecast indicators including flow demand,cumulative delay flight change rate,capacity change in the previous period and capacity change in period are extracted using congestion degree as a congestion evaluation index.Then based on genetic algorithm(GA)optimization back propagation(BP)neural network,a congestion prediction model is established to predict the degree of congestion.Finally,the actual data of an air traffic network flow system is used to analyze examples.The results show that the model has a good prediction and can provide a decision basis for the air traffic flow management department.