基于复杂网络的空中交通流量短期预测
Short-Term Prediction of Air Traffic Flow Based on Complex Network
王飞 1魏林琳2
作者信息
- 1. 中国民航大学空中交通管理学院,天津 300300
- 2. 黑龙江省机场管理集团有限公司哈尔滨太平国际机场飞行区管理部,哈尔滨 150079
- 折叠
摘要
为合理预测空中交通流量,结合复杂网络链路预测进行研究.首先,将时间序列转化为可视图得到拓扑特征量,然后结合基于局部信息、路径和随机游走的算法,比较在三亚不同扇区内的预测精度,发现RWR0.85算法预测精度最高.由于链路预测只能预测可能存在的连边,不能预测节点,因此引入D-S证据理论预测流量值,预测精度最高可达99.85%.结果表明,复杂网络链路预测结合D-S证据理论进行空中交通流量的预测是可行有效的,为进一步深入研究奠定了基础.
Abstract
To predict air traffic flow reasonably,research is conducted in conjunction with complex network link prediction.First,the time series is converted into a visual graph to obtain the topological feature quantity,and then combined with the algorithms based on local information,path,and random walk,the prediction accuracy in different sectors of Sanya is compared.It is found that the RWR0.85 algorithm has the highest prediction accuracy.Due to the fact that link prediction can only predict possible edges and cannot predict nodes,the D-S evidence theory is introduced to predict traffic values,with a maximum prediction accuracy of 99.85%.The results indicate that the combination of complex network link prediction and D-S evidence theory for predicting air traffic flow is feasible and effective,laying a foundation for further in-depth research.
关键词
复杂网络/空中交通流/链路预测/时间序列/D-S证据理论Key words
complex network/air traffic flow/link prediction/time series/D-S evidence theory引用本文复制引用
基金项目
天津市应用基础多元投入基金重点项目(21JCZDJC00840)
中央高校基本科研业务费专项资金项目(3122019129)
出版年
2024
国家科技期刊平台
NSTL
万方数据