考虑周期性波动因素的中长期空中交通流量预测

陈丹; 胡明华; 张洪海; 尹嘉男

doi:10.3969/j.issn.0258-2724.2015.03.028

考虑周期性波动因素的中长期空中交通流量预测

doi: 10.3969/j.issn.0258-2724.2015.03.028

基金项目:

国家自然科学基金资助项目(U1333202)

国家科技重大支撑计划资助项目(2011BAH24B08)

江苏省普通高校研究生科研创新计划资助项目(KYLX_0290)

详细信息

作者简介:
陈丹(1988-),女,博士研究生,研究方向为空中交通规划与管理,E-mail:sangyudang@163.com

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出版历程
- 收稿日期: 2014-05-27
- 刊出日期: 2015-06-25

Forecast Method for Medium-Long Term Air Traffic Flow Considering Periodic Fluctuation Factors

摘要

摘要: 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%.
- 空中交通管理 /
- 中长期流量预测 /
- 周期性波动 /
- 动态线性模型 /
- 贝叶斯理论
Abstract: 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%.
- air traffic management /
- medium-long term flow forecast /
- periodic fluctuation /
- dynamic linear model /
- Bayes theorem

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