基于集成学习算法的航路网络航段交通拥挤识别方法研究

交通运输系统工程与信息 ›› 2020, Vol. 20 ›› Issue (2): 166-173.

基于集成学习算法的航路网络航段交通拥挤识别方法研究

李桂毅^*a，郭铭宇^a，罗一帆^b

南京航空航天大学a. 民航/飞行学院；b. 航空学院，南京 211106

收稿日期:2019-10-17 修回日期:2019-12-18 出版日期:2020-04-25 发布日期:2020-04-30
作者简介:李桂毅(1982-)，男，湖北黄冈人，实验师，博士.
基金资助:
国家自然科学基金/ National Natural Science Foundation of China(U1333202)；国家级大学生创新创业训练计划项目/National Innovation and Entrepreneurship Training Program for College Students(2019CX00713)；南京航空航天大学2018 年度实验技术与开发项目/Experimental Technology and Development Project of NUAA in 2018(2018N03).

Traffic Congestion Identification of Air Route Network Segment Based on Ensemble Learning Algorithms

LI Gui-yi^a, GUO Ming-yu^a, LUO Yi-fan^b

a. College of Civil Aviation/College of Flight; b. College of Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Received:2019-10-17 Revised:2019-12-18 Online:2020-04-25 Published:2020-04-30

摘要/Abstract

摘要：

基于航路网络ADS-B航迹数据定义航路网络航段交通流量、航段交通密度、航段交通饱和度、航段交通接近率4 项交通拥挤状态评价指标；采用模糊C均值聚类算法和航段历史交通拥挤状态评价指标参数划分航段交通拥挤状态等级；结合集成学习算法构建航路网络航段交通拥挤状态识别模型，实现航段交通拥挤状态的识别. 实证分析表明：航路网络交通拥挤状态集成学习识别模型对实验航路网络航段交通拥挤状态识别准确率达到98.34%，采用决策树基学习器优于k 近邻基学习器，且增加的集成学习基学习器数量可提升模型的识别精度；集成学习识别模型的识别性能优于BP神经网络模型，识别方法符合实际且具有应用价值.

关键词: 航空运输, 交通拥挤识别, 集成学习, 航路网络, 模糊C均值聚类

Abstract:

This study uses four indicators for traffic congestion evaluations: air traffic flow, density, saturation, and approaching rate, based on the aircraft ADS-B track data in air route network. The level of traffic congestion on air route segments was defined through the fuzzy C-means clustering algorithm and the parameters of historical traffic congestion evaluation indicators. Based on ensemble learning algorithm, a traffic congestion status identification model was developed to identify the traffic congestion status of air route network. The empirical analysis results show that the accuracy rate of the proposed model is 98.34% in traffic congestion identifications. Decision tree- based learner performs better than k - nearest neighbor- based learner. Increasing the number of ensemble base learner could improve the identification accuracy of the model. The ensemble model performs better than the BP neural network model in terms of the identification performances. The identification method proposed in this study is practical and has certain application value.

Key words: air transportation, traffic congestion status identification, ensemble learning, air route network, fuzzy C-means clustering

中图分类号:

李桂毅，郭铭宇，罗一帆. 基于集成学习算法的航路网络航段交通拥挤识别方法研究[J]. 交通运输系统工程与信息, 2020, 20(2): 166-173.

LI Gui-yi, GUO Ming-yu, LUO Yi-fan. Traffic Congestion Identification of Air Route Network Segment Based on Ensemble Learning Algorithms[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(2): 166-173.

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链接本文: http://www.tseit.org.cn/CN/abstract/abstract20026.shtml

http://www.tseit.org.cn/CN/Y2020/V20/I2/166

参考文献

[1] 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, REN J, LI N. Identification of terminal area traffic situation based on FCM[J]. Aeronautical Computing Technique, 2014, 44(1): 1-8.]

[2] JIANG X R, WEN X X, WU M G, et al. A complex network analysis approach for identifying air traffic congestion based on independent component analysis[J]. Physica A, 2019, 523: 364-381.

[3] 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.]

[4] 张兆宁, 李蓝天. 基于空中交通流的空中交通网络拥挤程度评估[J]. 科学技术与工程, 2019, 19(1): 268- 272. [ZHANG Z N, LI L T. Crowding degree assessment of air traffic network based on air traffic flow[J]. Science Technology and Engineering, 2019, 19(1): 268-272.]

[5] 李桂毅, 胡明华, 郑哲. 基于FCM-粗糙集的多扇区交通拥挤识别方法研究[J]. 交通运输系统工程与信息, 2017, 17(6): 141-146. [LI G Y, HU M H, ZHENG Z. Multi-sector traffic congestion identification method based on FCM-rough sets[J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17(6): 141-146.]

[6] D'ANDREA E, MARCELLONI F. Detection of traffic congestion and incidents from GPS trace analysis[J]. Expert Systems with Applications, 2017(73): 43-56.

[7] REMPE F, HUBER G, BOGENBERGER K. Spatiotemporal congestion patterns in urban traffic networks [J]. Transportation Research Procedia, 2016(15): 513- 524.

[8] WANG J, TSAPAKIS I, ZHONG C. A space-time delay neural network model for travel time prediction[J]. Engineering Applications of Artificial Intelligence, 2016 (52): 145-160.

[9] 董春娇, 邵春福, 熊志华. 基于优化SVM的城市快速路网交通流状态判别[J]. 北京交通大学学报, 2011, 35 (6): 13-16. [DONG C J, SHAO C F, XIONG Z H. Identification of traffic states with optimized SVM method on urban expressway network[J]. Journal of Beijing Jiaotong University, 2011, 35(6): 13-16.]

[10] KUMAR K, PARIDA M, KATIYAR V K. Short term traffic flow prediction for a non urban highway using artificial neural network[J]. Procedia- Social and Behavioral Sciences, 2013(104): 755-764.

[11] ASENCIO-CORTÉS G, FLORIDO E, TRONCOSO A, et al. A novel methodology to predict urban traffic congestion with ensemble learning[J]. Soft Computing, 2016, 20(11): 4205-4216.

[12] FAN Z, SONG X, XIA T, et al. Online deep ensemble learning for predicting citywide human mobility[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2018, 2(3): 1- 21.

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