Air Traffic Flow Prediction Model Based on Improved Adding-Weighted One-Rank Local-rejion Method
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摘要: 空中交通流量精准预测是实施空中交通控制和管理的重要前提.针对空中交通流量时间序列的内在混沌动力特性，研究了基于改进加权一阶局域法的混沌交通流量时间序列预测模型.首先，提出了一种临近相点演化加权的改进一阶局域预测法，并通过在预测过程中构建误差序列进行预测结果修正；其次，利用关联维数出现饱和现象验证了4组不同统计时间间隔的实测空中交通流量时间序列均存在混沌特性；最后，在对空中交通流量时间序列进行相空间重构的基础上，利用改进加权一阶局域预测方法进行了流量预测结果的对比实验.结果表明，4组空中交通流量时间序列预测精度均有提高，时间尺度为10 min的流量预测效果最好，预测相对误差减小了29.7%.Abstract: Accurate air traffic flow prediction is an important basis for efficient air traffic control and management. Aiming at the inherent chaotic dynamic characteristics of air traffic flow time series, the chaotic traffic-flow time-series prediction model based on improved adding-weight one-rank local-region prediction method was analyzed herein. Firstly, an improved adding-weight one-rank local-region prediction method was proposed, which involved weighing the evolution of adjacent phase points. Further, the prediction results were corrected by construction of error sequences during the prediction process. Secondly, the chaotic characteristics were verified to exist in four groups of air traffic flow time series at different time scales, using the saturation phenomenon of correlation dimension. Finally, a validation experiment for air traffic flow prediction was carried out using the improved method, after phase space reconstruction of air traffic flow time series. The results show that the prediction accuracy of all four groups is improved, wherein the traffic flow time series with time scale of 10 min has the best precision; the relative error in this case reduces by 29.7%.
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图 2 Δt=10 min时的自相关系数函数
Figure 2. The auto correlative function of Traffic flow time series of 10 min
图 6 空中交通流量预测的平均绝对误差对比
Figure 6. Comparison Average absolute error of Air traffic flow prediction
图 7 空中交通流量预测的相对误差对比
Figure 7. Comparison Relative error of Air traffic flow prediction
表 1 不同时间尺度空中交通流量时间序列的时间延迟和嵌入维数
Table 1. Time delay and Embedding dimension of Air traffic flow time series under different time scales
参数 Δt=10 min Δt=7 min Δt=10 min Δt=15 min τ/min 2 3 3 4 m 6 9 7 4 
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