考虑航段相关性的航路拥挤态势多模型融合动态预测方法
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摘要
研究航路交通拥挤状态动态实时预测问题,可为缓解航路交通拥挤,优化拥挤管控策略提供科学的依据.首先,采用神经网络理论建立考虑航段相关性的交通流参数预测模型,预测航段流量和航段密度参数;然后,运用多模型融合预测算法提高预测精度,基于模糊C均值聚类算法和航段历史及预测交通流参数预测航段交通拥挤态势;最后,采用雷达实测航迹数据验证模型的有效性.研究结果表明,本文建立的预测模型同时考虑了时间和空间因素,对航路拥挤状态预测准确率达到82.29%,预测方法符合实际且对航路交通态势的预测具有应用价值;同时考虑航段相关性影响和采用多模型融合预测算法能够明显提高预测精度.
This paper studies the dynamic real-time prediction of air route traffic congestion, which aims at providing scientific basis to alleviate air route traffic congestion and optimize control strategies. First, based on the theory of neural network, a traffic flow parameter prediction model is established, taking the correlation of air route segment into consideration. Then a multi-model fusion prediction algorithm is adopted to improve forecast accuracy, and air route segment congestion situation is predicted based on fuzzy C-means clustering algorithm and previous and predicted traffic flow parameters of air route segment. Finally, the model is verified by ATC radar data. The results demonstrate that this model takes into account the factors of both space and time, and the prediction accuracy of air route congestion is 82.29%. The model corresponds to reality and is feasible for air route traffic states prediction. Meanwhile, consideration of the correlation effects of air route segments and prediction using multi-model fusion algorithm can significantly improve forecast accuracy.
This paper studies the dynamic real-time prediction of air route traffic congestion, which aims at providing scientific basis to alleviate air route traffic congestion and optimize control strategies. First, based on the theory of neural network, a traffic flow parameter prediction model is established, taking the correlation of air route segment into consideration. Then a multi-model fusion prediction algorithm is adopted to improve forecast accuracy, and air route segment congestion situation is predicted based on fuzzy C-means clustering algorithm and previous and predicted traffic flow parameters of air route segment. Finally, the model is verified by ATC radar data. The results demonstrate that this model takes into account the factors of both space and time, and the prediction accuracy of air route congestion is 82.29%. The model corresponds to reality and is feasible for air route traffic states prediction. Meanwhile, consideration of the correlation effects of air route segments and prediction using multi-model fusion algorithm can significantly improve forecast accuracy.
引文
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[8]杨磊,胡明华,尹苏皖,等.大型繁忙机场场面离场交通流拥堵特征分析[J].航空学报,2016,37(6):1921-1930.[YANG L,HU M H,YIN S W,et al.Characteristic analysis of departure traffic flow congestion in mega-airport suface[J].Acta Aeronautics et Astronautica Sinica,2016,37(6):1921-1930.]
[9]姜桂艳.道路交通状态判别技术与应用[M].北京:人民交通出版社,2004.[JIANG G Y.Technology and applications of the identification of road traffic condition[M].Beijing:China Communications Press,2004.]
[2]任其亮.时空路网交通拥堵预测与疏导决策方法研究[D].成都:西南交通大学,2007.[REN Q LResearch on method for traffic jam forecasting and leading-decision[D].Chengdu:Southwest Jiaotong University,2007.]
[3]姚智胜,邵春福,熊志华,等.基于主成分分析和支持向量机的道路网短时交通流量预测[J].吉林大学学报(工学版),2008,38(1):48-52.[YAO Z S,SHAO C FXIONG Z H,et al.Short-term traffic volumes forecasting of road network based on principa component analysis and support vector machine[J]Journal of Jilin University(Engineering and Technology Edition),2008,38(1):48-52.]
[4]董春娇,邵春福,周雪梅,等.基于交通流参数相关的阻塞流短时预测卡尔曼滤波算法[J].东南大学学报(自然科学版),2014,44(2):413-419.[DONG C J,SHAO C F,ZHOU X M,et al.Kalman filter algorithm for short-term jam traffic prediction based on traffic parameter correlation[J].Journal of Southeast University(Natural Science Edition),2014,44(2):413-419.]
[5]徐肖豪,任杰,李楠.基于FCM的终端区交通态势识别[J].航空计算技术,2014,44(1):1-8.[XU X H,RENJ,LI N.Identification of terminal area traffic situation based on FCM[J].Aeronautical Computing Technique,2014,44(1):1-8.]
[6]张洪海,许炎,张哲铭,等.终端区空中交通流参数模型与仿真[J].交通运输系统工程与信息,2014,14(6):58-64.[ZHANG H H,XU Y,ZHANG Z M,et al.Air traffic flow parameter model and simulation for airport terminal area[J].Journal of Transportation Systems Engineering and Information Technology,2014,14(6):58-64.]
[7]李善梅,徐肖豪,王超,等.基于灰色聚类的交叉航路拥挤识别方法[J].西南交通大学学报,2015,50(1):189-197.[LI S M,XU X H,WANG C,et al.Congestion identification of crossing air routes based on gray clustering method[J].Journal of Southwest Jiaotong University,2015,50(1):189-197.]
[8]杨磊,胡明华,尹苏皖,等.大型繁忙机场场面离场交通流拥堵特征分析[J].航空学报,2016,37(6):1921-1930.[YANG L,HU M H,YIN S W,et al.Characteristic analysis of departure traffic flow congestion in mega-airport suface[J].Acta Aeronautics et Astronautica Sinica,2016,37(6):1921-1930.]
[9]姜桂艳.道路交通状态判别技术与应用[M].北京:人民交通出版社,2004.[JIANG G Y.Technology and applications of the identification of road traffic condition[M].Beijing:China Communications Press,2004.]