| 基于改进加权一阶局域法的空中交通流量预测模型 |
| |
| 引用本文： | 王超,朱明,赵元棣.基于改进加权一阶局域法的空中交通流量预测模型[J].西南交通大学学报,2018,53(1):206-213. |
| |
| 作者姓名： | 王超 朱明 赵元棣 |
| |
| 作者单位： | 中国民航大学空中交通管理学院; |
| |
| 基金项目： | 国家自然科学基金民航联合基金资助项目U1533106国家自然科学基金民航联合基金资助项目U1433111 |
| |
| 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%.
|
| 关 键 词： | 混沌时间序列 相空间重构 加权一阶局域预测法 误差序列 空中交通流量预测 |
| 收稿时间： | 2016-10-25 |
Air Traffic Flow Prediction Model Based on Improved Adding-Weighted One-Rank Local-rejion Method |
| |
| WANG Chao,ZHU Ming,ZHAO Yuandi.Air Traffic Flow Prediction Model Based on Improved Adding-Weighted One-Rank Local-rejion Method[J].Journal of Southwest Jiaotong University,2018,53(1):206-213. |
| |
| Authors: | WANG Chao ZHU Ming ZHAO Yuandi |
| |
| 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%. |
| |
| Keywords: | |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《西南交通大学学报》浏览原始摘要信息 |
|
点击此处可从《西南交通大学学报》下载全文 |
|
