基于K近邻模型的空中交通流量短期预测
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摘要
为了准确预测空中交通短期流量,减轻空管协调压力,基于K近邻算法构建了空中交通短期预测模型。首先,通过多次取K值比较相对误差来确定合适的K值。之后,对原有的K近邻模型进行改进,引入空间参数,提出了3种状态向量组合的K近邻模型:时间维度模型、向台航路-时间维度模型与时空参数模型。以某扇区雷达数据对该模型进行检测,结果表明:同时引入时空参数的K近邻模型误差最小,平均为14.16%;基于指数权重的距离衡量方式均能达到预测精度优化的效果;高斯权重预测法在时间维度模型下优于反函数法,引入空间参数则反之;指数权重距离下的反函数法预测的时空参数模型误差为13.94%。改进后的K近邻模型对不同流量情况都具有普适性,预测结果可为空中交通流量管理提供理论参考。
It's worth to predict available short-term air traffic flow and reduce ATCO workload. An air traffic flow model is built based on K-nearest neighbor. First, relative errors from different K values are compared to determine the appropriate K values. After that, space parameter is introduced to improve the model. Then these three kinds of state vectors are combined and new K-nearest neighbor models are proposed including time dimension model, to route-time dimension model and time-space parameter model. Radar data within a certain sector is used to test K-neighbor model, showing out that K-nearest neighbor model with time-space parameter has minimum error,whose average error equals to 14.6%. Distance measuring method based on weight index can attain the goal of prediction accuracy optimization. Gaussian function can produce a better result under time parameter model while it is weaker when space parameter is taken into consideration. Statistics show prediction 's error is only 13.94% under the index weight distance method of inverse function model with time-space parameter. The improved K-nearest neighbor model has applicability for different traffic situations and strong portability for complicated air traffic situation of China.
It's worth to predict available short-term air traffic flow and reduce ATCO workload. An air traffic flow model is built based on K-nearest neighbor. First, relative errors from different K values are compared to determine the appropriate K values. After that, space parameter is introduced to improve the model. Then these three kinds of state vectors are combined and new K-nearest neighbor models are proposed including time dimension model, to route-time dimension model and time-space parameter model. Radar data within a certain sector is used to test K-neighbor model, showing out that K-nearest neighbor model with time-space parameter has minimum error,whose average error equals to 14.6%. Distance measuring method based on weight index can attain the goal of prediction accuracy optimization. Gaussian function can produce a better result under time parameter model while it is weaker when space parameter is taken into consideration. Statistics show prediction 's error is only 13.94% under the index weight distance method of inverse function model with time-space parameter. The improved K-nearest neighbor model has applicability for different traffic situations and strong portability for complicated air traffic situation of China.
引文
[1]刘永欣,赵德斌.基于管制员知识的终端区飞行冲突解决模型[J].中国民航大学学报,2016,34(3):6-8,16.
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[4]翁剑成,荣健,任福田,等.基于非参数回归的快速路行程速度短期预测算法[J].公路交通科技,2007,24(3):93-97:106.
[5]于滨,邬珊华,王明华,等.K近邻短时交通流预测模型[J].交通运输工程学报,2012,12(2):105-111.
[6]谢海红,戴许昊,齐远.短时交通流预测的改进K近邻算法[J].交通运输工程学报,2014,14(3):87-94.
[7]张涛,陈先,谢美萍,等.基于K近邻非参数回归的短时交通流预测方法[J].系统工程理论与实践,2010,30(2):376-384.
[8]熊亚军,廖晓农,李梓铭,等.KNN数据挖掘算法在北京地区霾等级预报中的应用[J].气象,2015,41(1):98-104.
[2]DAVIS G A,NIHAN N.Nonparametric regression and short-term freeway traffic forecasting[J].Journal of Transportation Engineering,1991,117(2):178-188.
[3]SMITH B L,DEMETSKY M J.Traffic flow forecasting:comparison of modeling approaches[J].American Society of Civil Engineers,1997,123(4):261-266.
[4]翁剑成,荣健,任福田,等.基于非参数回归的快速路行程速度短期预测算法[J].公路交通科技,2007,24(3):93-97:106.
[5]于滨,邬珊华,王明华,等.K近邻短时交通流预测模型[J].交通运输工程学报,2012,12(2):105-111.
[6]谢海红,戴许昊,齐远.短时交通流预测的改进K近邻算法[J].交通运输工程学报,2014,14(3):87-94.
[7]张涛,陈先,谢美萍,等.基于K近邻非参数回归的短时交通流预测方法[J].系统工程理论与实践,2010,30(2):376-384.
[8]熊亚军,廖晓农,李梓铭,等.KNN数据挖掘算法在北京地区霾等级预报中的应用[J].气象,2015,41(1):98-104.