空中交通流量预测建模与仿真研究

摘要

研究空中交通流量预测问题,由于航空流量密集,流量增大,造成延误.同时空中交通流量变化具有非线性、时变性等特点.根据线性传统预测方法不能准确描述交通流量变化规律,导致空中交通流量预测精度低.为了提高空中交通流量预测精度,提出一种灰色预测和支持向量机相结合的空中交通流量混合预测模型.混合预测模型先采用灰色模型对空中交通流量线性部分进行预测,然后采用支持向量机非线性部分进行预测,最后将两者结果相融合得到最终预测结果.仿真结果表明,混合模型提高了空中交通流量预测精度,克服了传统预测模型缺陷,为空中交通流量预测提供了依据和有效的方法.%Air traffic flow changes with nonlinear change characteristics, the traditional forecasting methods can-not describe the change rule and prediction accuracy is low. In order to improve the air traffic flow predictive accura-cy, an air traffic flow prediction model is proposed based on grey prediction and support vector machine (SVM). Firstlly, the model uses grey model to predict the air traffic flow linear laws, and uses support vector machine to pre-dict the grey model's residual sequence, reflecting its nonlinear variation. Lastly, two results are summariede to get air traffic flow prediction results. Simulation results show that prediction accuracy is better than single prediction re-sults. The model makes full use of the advantages of two models, and can accurately describe air traffic flow change rule and overcome the single model defects. It is more suitable for air traffic flow prediction.