基于KNN和SVR的航班滑出时间预测

冯霞; 孟金双

doi:10.3969/j.issn.0258-2724.2017.05.023

基于KNN和SVR的航班滑出时间预测

doi: 10.3969/j.issn.0258-2724.2017.05.023

冯霞^1,2,
孟金双¹

基金项目:

国家自然科学基金资助项目（61502499）

中国民航科技创新引导资金项目重大专项基金资助项目（MHRD20140105）

中央高校基本科研业务费项目资助（3122013C005，3122014D032，3122015D015）

详细信息

作者简介:
冯霞(1970-),女,教授,博士,研究方向为数据挖掘,民航信息智能处理,电话:13920988096,E-mail:xfeng@cauc.edu.cn

计量
- 文章访问数: 429
- HTML全文浏览量: 57
- PDF下载量: 82
- 被引次数: 0
出版历程
- 收稿日期: 2016-04-28
- 刊出日期: 2017-10-25

Flight Taxi-out Time Prediction Based on KNN and SVR

FENG Xia^1,2,
MENG Jinshuang¹

摘要

摘要: 针对大型繁忙机场粗放式预估航班滑出时间可能带来的场面交通不畅、运行效率不高等问题，基于K最近邻（KNN）和支持向量回归（SVR），构建了离港航班滑出时间预测模型.该模型采用KNN方法，考虑滑行距离、滑出过程中同一跑道正在滑出航班数、撤轮档后15 min内推出航班数等因素，预测得到航班滑出期间使用同一跑道的起降航班数；基于该预测结果，结合滑出距离和撤轮档前15 min同一跑道平均滑出时间等因素，采用SVR预测航班滑出时间.使用首都机场航班运行数据对模型进行检验，结果表明：在误差范围为±3 min内，平均预测准确率可达79.86%.
- 滑出时间预测 /
- K最近邻 /
- 支持向量回归 /
- 离港航班 /
- 滑行延误
Abstract: Aimed at resolving the possible ground traffic and low operational efficiency issues in a large busy airport caused by the simple aircraft taxi-out time estimation, a two-step aircraft taxi-out time prediction model was constructed based on K-nearest neighbours (KNN) and support vector regression (SVR). First, considering the influence of factors such as taxiing-out distance, the number of taxi-out flights using the same runway and the number of pushback complete flights launched within 15 min of the removal of chocks, the number of departures and arrivals using the same runway during the flight taxiing out was predicted based on KNN. Then, based on the prediction results and other influential factors such as the taxiing-out distance and the average taxi-out time using the same runway within 15 min of the removal of chocks, the taxi-out time was predicted based on SVR. The airport operation data was grouped by arrival and departure traffic flow and the prediction model of the taxi-out time was constructed separately for each group. The experimental results based on the actual operation data of Beijing Capital International Airport show that the average prediction accuracy of the proposed model is up to 79.86% within ±3 min.
- taxi-out time prediction /
- KNN /
- SVR /
- depature flight /
- taxi-out delay

HTML全文

参考文献(15)

SRIVASTAVA A. Improving departure taxi time predictions using ASDE-X surveillance data[C]//Proceedings of the 30th IEEE/AIAA International Conference on Digital Avionics Systems Conference. Seattle:Institute of Electrical and Electronics Engineers Inc., 2011:2B51-2B514.

RAVIZZA S, ATKIN J A D, MAATHUIS M H, et al. A combined statistical approach and ground movement model for improving taxi time estimations at airports[J]. Journal of the Operational Research Society, 2013, 64(9):1347-1360.

JORDAN R, ISHUTKINA M A, REYNO-LDS T G. A statistical learning approach to the modeling of aircraft taxi-time[R].[S.1.]:Federal Aviation Administration Washington D. C., 2010.

CHATTERJI G B, ZHENG Y. Wheels-Off time prediction using surface traffic metrics[C]//Proceedings of the 12th AIAA Aviation Technology, Integration, and Operations Conference and the 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Indianapolis:American Institute of Aeronautics and Astronautics Inc., 2012:1-14.

BALAKRISHNA P, GANESAN R, SHERRY L. Estimating taxi-out times with a reinforcement learning algorithm[C]//Proceedings of the 27th IEEE/AIAA Digital Avionics Systems Conference. Paul:Institute of Electrical and Electronics Engineers Inc., 2008:3D31-3D312

BALAKRISHNA P, GANESAN R, SHERRY L. Application of reinforcement learning algorithms for predicting taxi-out times[C]//Proceedings of the 8th ATM R & D Seminars. Napa:EUROCONTROL, 2009:255-261.

BALAKRISHNA P, GANESAN R, SHERRY L. Accuracy of reinforcement learning algorithms for predicting aircraft taxi-out times:a case-study of tampa bay departures[J]. Transportation Research Part C Emerg-ing Technologies, 2010, 18(6):950-962.

RAVIZZA S, CHEN J, ATKIN J A D, et al. Aircraft taxi time prediction:comparisons and insights[J]. Applied Soft Computing, 2014, 14(1):397-406.

CHEN J, RAVIZZA S, ATKIN J A D, et al. On the utilisation of fuzzy rule-based systems for taxi time estimations at airports[C]//Proceedings of the 11th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization and Systems. Saarbrucken:Schloss Dagstuhl-Leibniz-Zentrum Fur Informatik Gmbh, 2011:134-145.

薛雪,孙伟,梁睿. 露天煤矿卡车路段行程时间的实时动态预测新方法[J]. 煤炭学报,2012,37(8):1418-1422. XUE Xue, SUN Wei, LIANG Rui. A new method of real-time dynamic forecast of truck link travel time in open mines[J]. Journal of China Coal Socirty, 2012, 37(8):1418-1422.

WANG Zhuyun, PENG Zhongren. A PSO-SVM model for short-term travel time prediction based on bluetooth technology[J]. Journal of Harbin Institute of Technology, 2015, 22(3):7-14.

陈维荣,关佩,邹月娴. 基于SVM的交通事件检测技术[J]. 西南交通大学学报,2011,46(1):63-67. CHEN Weirong, GUAN Pei, ZOU Yuexian. Automatic incident detection technology based on SVM[J]. Journal of Southwest Jiaotong University, 2011, 46(1):63-67.

王翔,陈小鸿,杨祥妹. 基于K最近邻算法的高速公路短时行程时间预测[J]. 中国公路学报,2015,28(1):102-111. WANG Xiang, CHEN Xiaohong, YANG Xiangmei. Short term prediction of expressway travel time based on KNN algorithm[J]. China Journal of Highway and Transport, 2015, 28(1):102-111.

HAN J W, MICHELINE K, JIAN P. Data mining concepts and techniques[M]. Beijing:China Machine Press, 2012:265-270.

石柯,陈洪生,张仁同. 一种基于支持向量回归的802.11无线室内定位方法[J]. 软件学报,2014,25(11):2636-2651. SHI Ke, CHEN Hongsheng, ZHANG Rentong. Indoor location method based on support vector regression in 802.11 wireless environments[J]. Journal of Software, 2014, 25(11):2636-2651.

施引文献

附加材料(0)

访问统计

点击查看大图

计量

文章访问数: 429
HTML全文浏览量: 57
PDF下载量: 82
被引次数: 0

基于KNN和SVR的航班滑出时间预测

doi: 10.3969/j.issn.0258-2724.2017.05.023

作者简介: 冯霞(1970-),女,教授,博士,研究方向为数据挖掘,民航信息智能处理,电话:13920988096,E-mail:xfeng@cauc.edu.cn

计量

出版历程

Flight Taxi-out Time Prediction Based on KNN and SVR

计量

出版历程

目录

作者简介:
冯霞(1970-),女,教授,博士,研究方向为数据挖掘,民航信息智能处理,电话:13920988096,E-mail:xfeng@cauc.edu.cn