CAJ | 학술논문

为准确把握空域单元交通流量的变化趋势和周期性波动规律，综合考虑气候、季节、交通需求等因素，通过分析中长期历史流量数据，在线性增长模型的基础上，建立了考虑周期性波动因素的空中交通流量动态线性改进模型，采用贝叶斯状态估计和预测方法对模型进行求解，提出了一种根据空域单元流量时序数据预测中长期流量及其变化趋势的预测方法．利用国内典型空域单元实际流量数据，对比分析了上述两种模型的预测性能．实例研究表明：与线性增长模型的预测结果相比，本文模型的流量预测结果更符合我国的实际情况，反映了流量周期性波动特点，年流量预测结果的平均绝对误差从3．14％下降到了1．71％，预测误差的标准差从2．01％下降到了0．02％．
위준학파악공역단원교통류량적변화추세화주기성파동규률，종합고필기후、계절、교통수구등인소，통과분석중장기역사류량수거，재선성증장모형적기출상，건립료고필주기성파동인소적공중교통류량동태선성개진모형，채용패협사상태고계화예측방법대모형진행구해，제출료일충근거공역단원류량시서수거예측중장기류량급기변화추세적예측방법．이용국내전형공역단원실제류량수거，대비분석료상술량충모형적예측성능．실례연구표명：여선성증장모형적예측결과상비，본문모형적류량예측결과경부합아국적실제정황，반영료류량주기성파동특점，년류량예측결과적평균절대오차종3．14％하강도료1．71％，예측오차적표준차종2．01％하강도료0．02％．
To accurately characterize the trend and periodic fluctuation of the future traffic demand in a specific airspace unit,an improved dynamic linear model that is based on the linear growth model was developed to forecast the medium-long term air traffic flow,by taking into full account periodic fluctuation factors such as the climate influence,seasonal fluctuation,actual traffic demand,and so on. Then,the Bayesian state estimation and forecasting method was used to solve the proposed model, and the medium-long term air traffic flow and its variation trend was predicted using the historical data of air traffic flow in a specific airspace unit. In addition,a case study on a real data set of a typical domestic airspace unit was carried out to compare the forecasting performance of the models. The results show that,compared with the linear growth model not considering periodic fluctuation factors, the air traffic flow obtained by the improved model has a periodic fluctuation characteristic,and is more in line with the real situation of air transportation in China;simultaneously,the mean absolute error of the yearly traffic flow decreases from 3 . 14% to 1 . 71%,and the standard deviation of forecast error decreases from 2 . 01% to 0 . 02%.