Trajectory clustering for arrival aircraft via new trajectory representation

doi:10.23919/JSEE.2021.000040

Journal of Systems Engineering and Electronics ›› 2021, Vol. 32 ›› Issue (2): 473-486.doi: 10.23919/JSEE.2021.000040

• CONTROL THEORY AND APPLICATION • Previous Articles Next Articles

Trajectory clustering for arrival aircraft via new trajectory representation

Xuhao GUI(), Junfeng ZHANG*(), Zihan PENG()

¹ College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Received:2020-06-02 Online:2021-04-29 Published:2021-04-29
Contact: Junfeng ZHANG E-mail:ShowhowGui@outlook.com;zhangjunfeng@nuaa.edu.cn;fluff9797@163.com
About author:|GUI Xuhao was born in 1995. He received his B.S. degree in communications and transportation from Civil Aviation University of China in 2018. He is pursuing his M.S. degree in Nanjing University of Aeronautics and Astronautics. His research interests are air traffic management and machine learning. E-mail: ShowhowGui@outlook.com||ZHANG Junfeng was born in 1979. He received his Ph.D. degree in control theory and control engineering from Nanjing University of Aeronautics and Astronautics in 2008. He is currently an associate professor in College of Civil Aviation at Nanjing University of Aeronautics and Astronautics. His research interests include 4D trajectory generation, prediction and optimization, as well as decision support tool for the air traffic control. E-mail: zhangjunfeng@nuaa.edu.cn||PENG Zihan was born in 1997. She received her B.S. degree in communications and transportation from Nanjing Agricultural University in 2019. She is pursuing her M.S. degree in Nanjing University of Aeronautics and Astronautics. Her research interests are air traffic management and machine learning. E-mail: fluff9797@163.com
Supported by:
This work was supported by the Joint Fund of National Natural Science Foundation of China and Civil Aviation Administration of China (U1933117), and the Open Fund for Graduate Innovation Base (Laboratory) of Nanjing University of Aeronautics and Astronautics (kfjj20190709);This work was supported by the Joint Fund of National Natural Science Foundation of China and Civil Aviation Administration of China (U1933117), and the Open Fund for Graduate Innovation Base (Laboratory) of Nanjing University of Aeronautics and Astronautics (kfjj20190709)

Abstract

Abstract:

Trajectory clustering can identify the flight patterns of the air traffic, which in turn contributes to the airspace planning, air traffic flow management, and flight time estimation. This paper presents a semantic-based trajectory clustering method for arrival aircraft via new proposed trajectory representation. The proposed method consists of four significant steps: representing the trajectories, grouping the trajectories based on the new representation, measuring the similarities between different trajectories through dynamic time warping (DTW) in each group, and clustering the trajectories based on k-means and density-based spatial clustering of applications with noise (DBSCAN). We take the inbound trajectories toward Shanghai Pudong International Airport (ZSPD) to carry out the case studies. The corresponding results indicate that the proposed method could not only distinguish the particular flight patterns, but also improve the performance of flight time estimation.

Key words: air traffic management, trajectory clustering, trajectory representation, flight pattern

Xuhao GUI, Junfeng ZHANG, Zihan PENG. Trajectory clustering for arrival aircraft via new trajectory representation[J]. Journal of Systems Engineering and Electronics, 2021, 32(2): 473-486.

Figures/Tables 19

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Fig.11

Fig.12

Fig.11

Fig.13

Table 1

Fig.14

Fig.15

Table 2

References 23

1	KISTAN T, GARDI A, SABATINI R, et al An evolutionary outlook of air traffic flow management techniques. Progress in Aerospace Sciences, 2017, 88, 15- 42. doi: 10.1016/j.paerosci.2016.10.001
2	ALESSANDRO G, ROBERTO S, TREVOR K Multi-objective 4D trajectory optimization for integrated avionics and air traffic management systems. IEEE Trans. on Aerospace and Electronic Systems, 2018, 55 (1): 170- 181.
3	LI M Z, RYERSON M S Reviewing the DATAS of aviation research data: diversity, availability, tractability, applicability, and sources. Journal of Air Transport Management, 2019, 75, 111- 130. doi: 10.1016/j.jairtraman.2018.12.004
4	REHM F. Clustering of flight tracks. Proc. of the American Institute of Aeronautics and Astronautics, 2010, 1- 9.
5	GARIEL M, SRIVASTAVA A N, FERON E Trajectory clustering and an application to airspace monitoring. IEEE Trans. on Intelligent Transportation Systems, 2011, 12 (4): 1511- 1524. doi: 10.1109/TITS.2011.2160628
6	ANDRIENKO G, ANDRIENKO N, FUCHS G, et al. Clustering trajectories by relevant parts for air traffic analysis. IEEE Trans. on Visualization and Computer Graphics, 2017, 24 (1): 34- 44.
7	OLIVE X, MORIO J Trajectory clustering of air traffic flows around airports. Aerospace Science and Technology, 2019, 84, 776- 781. doi: 10.1016/j.ast.2018.11.031
8	MARZUOLI A, GARIEL M, VELA A, et al. Data-based modeling and optimization of en route traffic. Journal of Guidance, Control, and Dynamics, 2014, 37 (6): 1930- 1945. doi: 10.2514/1.G000010
9	BOMBELLI A, SOLER L, TRUMBAUER E, et al. Strategic air traffic planning with Fréchet distance aggregation and rerouting. Journal of Guidance, Control, and Dynamics, 2017, 40 (5): 1117- 1129. doi: 10.2514/1.G002308
10	BOMBELLI A, TORNÉ A S, TRUMBAUER E, et al. Improved clustering for route-based eulerian air traffic modeling. Journal of Guidance, Control, and Dynamics, 2019, 42 (5): 1064- 1077. doi: 10.2514/1.G003939
11	GERDES I, TEMME A, SCHULTZ M. Dynamic airspace sectorisation for flight-centric operations. Transportation Research Part C: Emerging Technologies, 2018, 95, 460- 480. doi: 10.1016/j.trc.2018.07.032
12	MURÇA M C R, HANSMAN R J, LI L, et al. Flight trajectory data analytics for characterization of air traffic flows: a comparative analysis of terminal area operations between New York, Hong Kong and Sao Paulo. Transportation Research Part C: Emerging Technologies, 2019, 97, 324- 347.
13	MURCA M C R, HANSMAN R J Identification, characterization, and prediction of traffic flow patterns in multi-airport systems. IEEE Trans. on Intelligent Transportation Systems, 2018, 20 (5): 1683- 1696.
14	CARMONA M A A, NIETO F S, GALLEGO C E V A data-driven methodology for characterization of a terminal manoeuvring area in multi-airport systems. Transportation Research Part C: Emerging Technologies, 2020, 111, 185- 209. doi: 10.1016/j.trc.2019.12.011
15	HONG S, LEE K Trajectory prediction for vectored area navigation arrivals. Journal of Aerospace Information Systems, 2015, 12 (7): 490- 502. doi: 10.2514/1.I010245
16	WANG Z Y, LIANG M, DELAHAYE D A hybrid machine learning model for short-term estimated time of arrival prediction in terminal manoeuvring area. Transportation Research Part C: Emerging Technologies, 2018, 95, 280- 294. doi: 10.1016/j.trc.2018.07.019
17	ZHENG Y Trajectory data mining: an overview. ACM Trans. on Intelligent Systems and Technology, 2015, 6 (3): 1- 41.
18	YUAN G, SUN P H, ZHAO J, et al. A review of moving object trajectory clustering algorithms. Artificial Intelligence Review, 2017, 47 (1): 123- 144. doi: 10.1007/s10462-016-9477-7
19	MORRIS B T, TRIVEDI M M Trajectory learning for activity understanding: unsupervised, multilevel, and long-term adaptive approach. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2011, 33 (11): 2287- 2301. doi: 10.1109/TPAMI.2011.64
20	ZHAO L B, SHI G Y A trajectory clustering method based on Douglas-Peucker compression and density for marine traffic pattern recognition. Ocean Engineering, 2019, 172, 456- 467. doi: 10.1016/j.oceaneng.2018.12.019
21	SALAUN E, GARIEL M, VELA A E, et al. Aircraft proximity maps based on data-driven flow modeling. Journal of Guidance, Control, and Dynamics, 2012, 35 (2): 563- 577. doi: 10.2514/1.53859
22	LEE J G, HAN J, WHANG K Y Trajectory clustering: a partition-and-group framework. Proc. of the ACM SIGMOD International Conference on Management of Data, 2007, 593- 604.
23	MAHBOUBI Z, KOCHENDERFER M J Learning traffic patterns at small airports from flight tracks. IEEE Trans. on Intelligent Transportation Systems, 2017, 18 (4): 917- 926. doi: 10.1109/TITS.2016.2598064

Fix	Group	Mean	Median	Confidence level (95% )			Confidence level (75%)
Fix	Group	Mean	Median	Lower limit	Upper limit	Interval	Lower limit	Upper limit	Interval
BK	Total	16.3	16.1	13.4	19.3	5.9	14.6	18.1	3.5
	C1	15.6	15.5	13.7	17.4	3.7	14.5	16.6	2.1
	C2	16.6	16.5	14.5	18.6	4.1	15.4	17.7	2.3
	C3	18.0	18.1	15.9	20.1	4.2	16.8	19.2	2.4
	C4	20.8	20.6	17.7	23.8	6.1	19.0	22.6	3.6
	C5	19.5	19.8	16.9	22.0	5.1	18.0	21.0	3.0
SASAN	Total	21.3	21.0	17.4	25.2	7.8	19.0	23.6	4.6
	C1	19.6	19.4	16.4	22.7	6.3	17.7	21.4	3.7
	C2	21.1	20.9	18.2	24.1	5.9	19.4	22.9	3.5
	C3	20.7	20.5	17.8	23.6	5.8	19.0	22.4	3.4
	C4	23.5	23.7	20.7	26.3	5.6	21.8	25.1	3.3
	C5	24.1	24.5	20.3	27.9	7.6	21.9	26.3	4.4

Flight pattern	Standard	Short-cut	Dog-leg	Parallel-offset
Standard	0.00	130.00	358.86	21.50
Short-cut	130.00	0.00	317.50	129.62
Dog-leg	358.86	317.50	0.00	322.93
Parallel-offset	21.50	129.62	322.93	0.00

Trajectory clustering for arrival aircraft via new trajectory representation

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

Share this article

Figures/Tables 19

References 23

Related Articles 1

Recommended Articles

Metrics

Comments