Analysis of Influence Factors for Unstable Approach in Fine⁃Grained Scale

XIE Jiayi; SUN Huabo; WANG Chun; LU Binbin

doi:10.13203/j.whugis20190120

Volume 46 Issue 8

Aug. 2021

Turn off MathJax

Article Contents

Abstract

References

Geomatics and Information Science of Wuhan University > 2021 > 46(8): 1201-1208. > DOI: 10.13203/j.whugis20190120

XIE Jiayi, SUN Huabo, WANG Chun, LU Binbin. Analysis of Influence Factors for Unstable Approach in Fine⁃Grained Scale[J]. Geomatics and Information Science of Wuhan University, 2021, 46(8): 1201-1208. DOI: 10.13203/j.whugis20190120

Citation:

PDF (11663 KB)

Analysis of Influence Factors for Unstable Approach in Fine⁃Grained Scale

1.
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China
2.
Institute of Aviation Safety, China Academy of Civil Aviation Science and Technology, Beijing 100028, China

Funds:

The National Natural Science Foundation of China U1833201

More Information

Author Bio:
XIE Jiayi, master, specializes in spatial statistics and geographically weighted models.JiayiXie@whu.edu.cn
Corresponding author:
LU Binbin, PhD, associate professor. E-mail: binbinlu@whu.edu.cn
Received Date: November 19, 2019
Published Date: August 04, 2021

Graphical Abstract

Abstract

Abstract

Objectives Unstable approach is one of the most important risks that threatens the flight safety during the descending phase, and is affected by factors such as meteorological and topographical that have strong spatial heterogeneity.
Methods In this article, we use the big data of quick access recorder (QAR) that collected by China Academy of Civil Aviation Science and Technology (CAST) to detect the unstable approach happening in Airbus and Boeing aircrafts in January 2018, and utilize exploratory spatial data analysis methods to explore the spatial patterns and relative influencing factors. In addition, Pearson correlation coefficient and geographically weighted correlation coefficient are used to investigate the pair-wise relationships between unstable approach and factors.
Results Experimental results show that unstable approaches of different aircraft types are distinctive in spatial distributions, and unstable approach of same aircraft type spatially varies in different regions in China. Besides, the correlation description in the fine-grained scale is better than in the global scale.
Conclusions This study provides an important research basis and guidance for the future quantitative cause analysis, which is significant for avoiding such risks.
- unstable approach,
- flight safety,
- spatial heterogeneity,
- space-time cube,
- geographically weighted summary statistics

FullText(HTML)

References (19)

References

[1]	杜红兵, 李珍香. 进近着陆运输飞行事故原因及预防对策研究[J]. 中国安全科学学报, 2006, 16(6): 118-122 doi: 10.3969/j.issn.1003-3033.2006.06.022 Du Hongbing, Li Zhenxiang. Cause Analysis on Approach-and-Landing Loss Accidents and Their Countermeasures[J]. China Safety Science Journal, 2006, 16(6): 118-122 doi: 10.3969/j.issn.1003-3033.2006.06.022
[2]	陈亚青, 孙宏. 进近管制员工作进程分类及工作负荷研究[J]. 中国安全科学学报, 2006, 16(2): 65-68 doi: 10.3969/j.issn.1003-3033.2006.02.013 Chen Yaqing, Sun Hong. Study on Classification of Work Processes and Workload of Approaching Controller[J]. China Safety Science Journal, 2006, 16(2): 65-68 doi: 10.3969/j.issn.1003-3033.2006.02.013
[3]	霍志勤, 罗帆. 近十年中国民航事故及事故征候的统计分析[J]. 中国安全科学学报, 2006, 16(12): 65-71 doi: 10.3969/j.issn.1003-3033.2006.12.013 Huo Zhiqin, Luo Fan. Statistic Analysis on Accidents and Incidents in the Last Decade in China Civil Aviation[J]. China Safety Science Journal, 2006, 16(12): 65-71 doi: 10.3969/j.issn.1003-3033.2006.12.013
[4]	刘方正, 范国磊, 马龙骧. 微下冲气流对飞机着陆性能的影响[J]. 海军航空工程学院学报, 2013(6): 639-642 https://www.cnki.com.cn/Article/CJFDTOTAL-HJHK201306012.htm Liu Fangzheng, Fan Guolei, Ma Longxiang. Influence of Micro-Downburst on Aircraft Landing Performance[J]. Journal of Naval Aeronautical and Astronautical University, 2013(6): 639-642 https://www.cnki.com.cn/Article/CJFDTOTAL-HJHK201306012.htm
[5]	周长春, 胡栋栋. 基于灰色聚类方法的航空公司飞机进近着陆阶段安全性评估[J]. 中国安全生产科学技术, 2012, 8(7): 99-102 https://www.cnki.com.cn/Article/CJFDTOTAL-LDBK201207023.htm Zhou Changchun, Hu Dongdong. Safety Assessment of Aircraft During Approach Landing Stage Based on Grey Clustering Method[J]. Journal of Safety Science and Technology, 2012, 8(7): 99-102 https://www.cnki.com.cn/Article/CJFDTOTAL-LDBK201207023.htm
[6]	郭媛媛, 孙有朝, 李龙彪, 等. 民用飞机进近着陆阶段灾难事故类型预测[J]. 航空计算技术, 2016, 16(4): 31-34 doi: 10.3969/j.issn.1671-654X.2016.04.008 Guo Yuanyuan, Sun Youchao, Li Longbiao, et al. Prediction of Catastrophic Accident Types of Civil Aircraft at Approach and Landing Phases[J]. Aeronautical Computing Technique, 2016, 16(4): 31-34 doi: 10.3969/j.issn.1671-654X.2016.04.008
[7]	Hanifa A, Akbar S. Detection of Unstable Approaches in Flight Track with Recurrent Neural Network[C]// International Conference on Information and Communications Technology, Yogyakarta, Indonesia, 2018
[8]	Wang Z, Sherry L, Shortle J F. Feasibility of Using Historical Flight Track Data to Nowcast Unstable Approaches[C]// Integrated Communications Navigation and Surveillance, Herndon, VA, USA, 2016
[9]	王超, 郭九霞, 沈志鹏. 基于基本飞行模型的4D航迹预测方法[J]. 西南交通大学学报, 2009, 44(2): 295-300 doi: 10.3969/j.issn.0258-2724.2009.02.028 Wang Chao, Guo Jiuxia, Shen Zhipeng. Prediction of 4D Trajectory Based on Basic Flight Models[J]. Journal of Southwest Jiaotong University, 2009, 44(2): 295-300 doi: 10.3969/j.issn.0258-2724.2009.02.028
[10]	Wang L, Wu C, Sun R. An Analysis of Flight Quick Access Recorder (QAR) Data and Its Applications in Preventing Landing Incidents[J]. Reliability Engineering and System Safety, 2014, 127: 86-96 doi: 10.1016/j.ress.2014.03.013
[11]	Wang Q, Wu K, Zhang T, et al. Aerodynamic Modeling and Parameter Estimation from QAR Data of an Airplane Approaching a High-Altitude Airport[J]. Chinese Journal of Aeronautics, 2012, 25(3): 361-371 doi: 10.1016/S1000-9361(11)60397-X
[12]	耿宏, 揭俊. 基于QAR数据的飞机巡航段燃油流量回归模型[J]. 航空发动机, 2008, 34(4): 46-50 https://www.cnki.com.cn/Article/CJFDTOTAL-HKFJ200804015.htm Geng Hong, Jie Jun. Fuel Flow Regression Model of Aircraft Cruise Based on QAR Data[J]. Aeroengine, 2008, 34(4): 46-50 https://www.cnki.com.cn/Article/CJFDTOTAL-HKFJ200804015.htm
[13]	Brunsdon C, Fotheringham A S, Charlton M. Geographically Weighted Summary Statistics: A Framework for Localised Exploratory Data Analysis[J]. Computers, Environment and Urban Systems, 2002, 26(6): 501-524 doi: 10.1016/S0198-9715(01)00009-6
[14]	Tobler W R. A Computer Movie Simulating Urban Growth in the Detroit Region[J]. Economic Geography, 1970, 46(2): 234-240 http://www.bioone.org/servlet/linkout?suffix=i1100-9233-18-5-711-b43&dbid=16&doi=10.1658%2F1100-9233(2007)18[711%3AUSOAPI]2.0.CO%3B2&key=10.2307%2F143141
[15]	Gollini I, Lu B, Charlton M, et al. GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models[J]. Journal of Statistical Software, 2014, 63(17), DOI: 10.18637/jss.v063.i17
[16]	Lu B, Harris P, Charlton M, et al. The GWmodel R Package: Further Topics for Exploring Spatial Heterogeneity Using Geographically Weighted Models[J]. Geo-Spatial Information Science, 2014, 17(2): 85-101 http://d.wanfangdata.com.cn/Periodical/dqkjxxkxxb-e201402002
[17]	Brunsdon C, Fotheringham A S, Charlton M E. Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity[J]. Geographical Analysis, 1996, 28(4): 281-298 doi: 10.1111/j.1538-4632.1996.tb00936.x/abstract
[18]	Nakaya T, Fotheringham A S, Brunsdon C, et al. Geographically Weighted Poisson Regression for Disease Association Mapping[J]. Statistics in Medicine, 2005, 24(17): 2 695-2 717 http://injuryprevention.bmj.com/lookup/external-ref?access_num=16118814&link_type=MED&atom=
[19]	Atkinson P M, German S E, Sear D A, et al. Exploring the Relations Between Riverbank Erosion and Geomorphological Controls Using Geographically Weighted Logistic Regression[J]. Geographical Analysis, 2003, 35(1): 58-82 http://www.tandfonline.com/servlet/linkout?suffix=cit0001&dbid=16&doi=10.1080%2F15568318.2017.1422301&key=10.1111%2Fj.1538-4632.2003.tb01101.x

[1]	LU Binbin, TIAN Xiaoxi, QIN Sixian, SHI Yilin, LI Jiansong. Urban Land Carrying Capacity Evaluation of Wuhan City with Geographically Weighted Techniques[J]. Geomatics and Information Science of Wuhan University, 2025, 50(3): 430-438. DOI: 10.13203/j.whugis20220778
[2]	ZHANG Linyi, SUN Huabo, WANG Chun, YU Changhui, LU Binbin. Spatiotemporal Pattern of Air Turbulence Risks with QAR Flight Big Data[J]. Geomatics and Information Science of Wuhan University, 2024, 49(3): 482-490. DOI: 10.13203/j.whugis20210616
[3]	JIANG Dong, ZHAO Wenji, WANG Yanhui, WAN Biyu. Analysis of Urban Road Spatiotemporal Situation by Geographically Weighted Regression with Spatial Grid Computing Method[J]. Geomatics and Information Science of Wuhan University, 2023, 48(6): 988-996. DOI: 10.13203/j.whugis20210173
[4]	LU Binbin, GE Yong, QIN Kun, ZHENG Jianghua. A Review on Geographically Weighted Regression[J]. Geomatics and Information Science of Wuhan University, 2020, 45(9): 1356-1366. DOI: 10.13203/j.whugis20190346
[5]	YAN Jinbiao, DUAN Xiaoqi, ZHENG Wenwu, LIU Yuan, DENG Yunyuan, HU Zui. An Adaptive IDW Algorithm Involving Spatial Heterogeneity[J]. Geomatics and Information Science of Wuhan University, 2020, 45(1): 97-104. DOI: 10.13203/j.whugis20180213
[6]	HU Xuemin, YU Jin, DENG Chongyang, SONG Sheng, CHEN Qin. Abnormal Crowd Behavior Detection and Location Based on Spatial-temporal Cube[J]. Geomatics and Information Science of Wuhan University, 2019, 44(10): 1530-1537. DOI: 10.13203/j.whugis20170424
[7]	LIU Jiping, DONG Chun, KANG Xiaochen, QIU Shike, ZHAO Rong, LI Bin, SUN Lijian. National Geographical Conditions Statistical Analysis in the Era of Big Data[J]. Geomatics and Information Science of Wuhan University, 2019, 44(1): 68-76, 83. DOI: 10.13203/j.whugis20180420
[8]	WANG Xiaoying, DAI Ziqiang, CAO Yunchang, SONG Lianchun. Weighted Mean Temperature T_m Statistical Analysis in Ground-based GPS in China[J]. Geomatics and Information Science of Wuhan University, 2011, 36(4): 412-416.
[9]	SHAO Zhenfeng, LIU Jun, LI Deren. A New Spatial Projection Image Fusion Method Based on Gaussian Image Cube[J]. Geomatics and Information Science of Wuhan University, 2010, 35(10): 1207-1211.
[10]	ZOU Yijiang, LI Deren, WANG Renxiang. Principle of Analytical Operation of Spatial Data Cube[J]. Geomatics and Information Science of Wuhan University, 2004, 29(9): 822-826.

Cited By

Cited by

Periodical cited type(5)

1.	张林意，孙华波，王纯，余长慧，卢宾宾. 基于QAR飞行大数据的空中颠簸风险时空分布模式探索与分析. 武汉大学学报(信息科学版). 2024(03): 482-490 .
2.	卢晓光，许忠睿，张喆，文贵宏. ECOD算法在飞机不稳定进近检测中的应用. 安全与环境学报. 2024(05): 1872-1878 .
3.	汪磊，李蕊君，王菲茵. 基于QAR数据与互信息法的进近风险评估模型. 交通信息与安全. 2024(04): 21-29+41 .
4.	廖易，张加龙，鲍瑞，许冬凡，王书贤，韩冬阳. 引入地形因子的高山松地上生物量动态估测. 生态学杂志. 2023(05): 1243-1252 .
5.	崔昊，张申利，任海军，韩连伟. 基于卷积神经网络的不稳定进近研究与应用. 航空计算技术. 2023(05): 20-23+28 .

Other cited types(5)

Get Citation

PDF

XML

Article views (1233) PDF downloads (58) Cited by(10)

Analysis of Influence Factors for Unstable Approach in Fine⁃Grained Scale

Abstract

References

Related Articles

Cited by

Periodical cited type(5)

Other cited types(5)

Catalog

Related

Analysis of Influence Factors for Unstable Approach in Fine⁃Grained Scale

Abstract

References

Related Articles

Cited by

Periodical cited type(5)

Other cited types(5)

Catalog

Related

Export File

Citation

Format

Content