A deep multimodal fusion and multitasking trajectory prediction model for typhoon trajectory prediction to reduce flight scheduling cancellation

doi:10.23919/JSEE.2024.000042

Journal of Systems Engineering and Electronics ›› 2024, Vol. 35 ›› Issue (3): 666-678.doi: 10.23919/JSEE.2024.000042

• SYSTEMS ENGINEERING • Previous Articles

A deep multimodal fusion and multitasking trajectory prediction model for typhoon trajectory prediction to reduce flight scheduling cancellation

Jun TANG(), Wanting QIN(), Qingtao PAN(), Songyang LAO()

College of Systems Engineering, National University of Defense Technology, Changsha 410000, China

Received:2022-05-17 Online:2024-06-18 Published:2024-06-19
Contact: Jun TANG E-mail:tangjun06@nudt.edu.cn;qinwt@nudt.edu.cn;panqingtao@nudt.edu.cn;laosongyang@vip.sina.com
About author:
TANG Jun was born in 1988. He was dedicated to his Ph.D. research in the Technical Innovation Cluster on Aeronautical Management, Autonomous University of Barcelona. He is currently an assistant professor at the Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology. His research interests include logistic systems, causal modelling, state space, air traffic management, and discrete event simulation. E-mail: tangjun06@nudt.edu.cn

QIN Wanting was born in 1996. She received her B.S. and M.S. degrees from Shijiazhuang Tiedao University in 2011 and 2015, respectively, and now is working for her Ph.D. degree at the College of Systems Engineering, National University of Defense Technology. Her research interests include trajectory data mining, artificial intelligent, state space and deep learning. E-mail: qinwt@nudt.edu.cn

PAN Qingtao was born in 1996. He received his B.S. degree from Ocean University of China in 2020. He is currently a Ph.D. student in the Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, majoring in control science and engineering. His research interests include intelligent optimization algorithm, system simulation and cluster control. E-mail: panqingtao@nudt.edu.cn

LAO Songyang was born in 1968. He received his B.S. degree in information system engineering and Ph.D. degree in system engineering from the National University of Defense Technology, Changsha, China, in 1990 and 1996, respectively. He joined National University of Defense Technology as a faculty member, in 1996, where he is currently a professor and the dean of the College of Systems Engineering. He was a visiting scholar with Dublin City University, Irish, from 2004 to 2005. His research interests include image processing, video analysis and human-computer interaction. E-mail: laosongyang@vip.sina.com
Supported by:
This work was supported by the National Natural Science Foundation of China (62073330).

Abstract

Abstract:

Natural events have had a significant impact on overall flight activity, and the aviation industry plays a vital role in helping society cope with the impact of these events. As one of the most impactful weather typhoon seasons appears and continues, airlines operating in threatened areas and passengers having travel plans during this time period will pay close attention to the development of tropical storms. This paper proposes a deep multimodal fusion and multitasking trajectory prediction model that can improve the reliability of typhoon trajectory prediction and reduce the quantity of flight scheduling cancellation. The deep multimodal fusion module is formed by deep fusion of the feature output by multiple submodal fusion modules, and the multitask generation module uses longitude and latitude as two related tasks for simultaneous prediction. With more dependable data accuracy, problems can be analysed rapidly and more efficiently, enabling better decision-making with a proactive versus reactive posture. When multiple modalities coexist, features can be extracted from them simultaneously to supplement each other’s information. An actual case study, the typhoon Lichma that swept China in 2019, has demonstrated that the algorithm can effectively reduce the number of unnecessary flight cancellations compared to existing flight scheduling and assist the new generation of flight scheduling systems under extreme weather.

Key words: flight scheduling optimization, deep multimodal fusion, multitasking trajectory prediction, typhoon weather, flight cancellation, prediction reliability

Jun TANG, Wanting QIN, Qingtao PAN, Songyang LAO. A deep multimodal fusion and multitasking trajectory prediction model for typhoon trajectory prediction to reduce flight scheduling cancellation[J]. Journal of Systems Engineering and Electronics, 2024, 35(3): 666-678.

Figures/Tables 9

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References 40

1	GUI G, LIU F, SUN J L, et al Flight delay prediction based on aviation big data and machine learning. IEEE Trans. on Vehicular Technology, 2020, 69 (1): 140- 150. doi: 10.1109/TVT.2019.2954094
2	WANG H J, SUN J Q, FAN K Relationships between the North Pacific Oscillation and the typhoon/hurricane frequencies. Science in China, 2007, 50 (9): 1409- 1416.
3	KOESDWIADY A, SOUA R, KARRAY F Improving traffic flow prediction with weather information in connected cars: a deep learning approach. IEEE Trans. on Vehicular Technology, 2016, 65 (12): 9508- 9517. doi: 10.1109/TVT.2016.2585575
4	ARAB A, TEKIN E, KHODAEI A, et al System hardening and condition-based maintenance for electric power infrastructure under hurricane effects. IEEE Trans. on Reliability, 2016, 65 (3): 1457- 1470. doi: 10.1109/TR.2016.2601818
5	LI Y H, WANG A J, QIAO L, et al. The impact of typhoon Morakot on the modern sedimentary environment of the mud deposition center off the Zhejiang–Fujian coast, China. Continental Shelf Research, 2012, 37: 92−100.
6	GUO T J, LI G S Study on methods to identify the impact factors of economic losses due to typhoon storm surge based on confirmatory factor analysis. Natural Hazards, 2020, 100 (2): 515- 534. doi: 10.1007/s11069-019-03823-w
7	ETANI N Development of a predictive model for on-time arrival flight of airliner by discovering correlation between flight and weather data. Journal of Big Data, 2019, 6 (1): 85- 98. doi: 10.1186/s40537-019-0251-y
8	XU Y, DALMAU R, MELGOSA M, et al A framework for collaborative air traffic flow management minimizing costs for airspace users: enabling trajectory options and flexible pre-tactical delay management. Transportation Research Part B: Methodological, 2020, 134, 229- 255. doi: 10.1016/j.trb.2020.02.012
9	CAO Y K, ZHU C P, WANG Y J, et al. A method of reducing flight delay by exploring internal mechanism of flight delays. Journal of Advanced Transportation, 2019, 2019: 7069380.
10	KHAKSAR H, SHEIKHOLESLAMI A A model for incorporating robustness into flight planning. Proceedings. of the Institution of Civil Engineers-Transport, 2018, 171 (5): 275- 285. doi: 10.1680/jtran.15.00119
11	WEI M, SUN B, WU W, et al A multiple objective optimization model for aircraft arrival and departure scheduling on multiple runways. Mathematical Biosciences and Engineering, 2020, 17 (5): 5545- 5560. doi: 10.3934/mbe.2020298
12	TANG F, LIU S, DONG X Y, et al. Aircraft ground service scheduling problems and partheno-genetic algorithm with hybrid heuristic rule. Proc. of the IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, 2017: 551−555.
13	CECEN R K, CETEK C, KAYA O Aircraft sequencing and scheduling in TMAs under wind direction uncertainties. Aeronautical Journal New Series, 2020, 124 (1282): 1896- 1912. doi: 10.1017/aer.2020.68
14	ABDELGHANY A, ABDELGHANY K, AZADIAN F. Airline flight schedule planning under competition. Computers & Operations Research, 2017, 87: 20−39.
15	WANG Z J, ZHANG X J. An approach of flight scheduling optimization meet the time conformance monitoring. Proc. of the 13th World Congress on Intelligent Control and Automation, 2018: 1647−1651.
16	KENAN N, JEBALI A, DIABAT A An integrated flight scheduling and fleet assignment problem under uncertainty. Computers & Operations Research, 2018, 100, 333- 342.
17	XIAO M M, CAI K Q, ABBASS H A Hybridized encoding for evolutionary multi-objective optimization of air traffic network flow: a case study on China. Transportation Research, Part E. Logistics and Transportation Review, 2018, 115, 35- 55. doi: 10.1016/j.tre.2018.04.011
18	SAFAK O, CAVUS O, AKTURK M. Multi-stage airline scheduling problem with stochastic passenger demand and non-cruise times. Transportation Research, Part B: Methodological, 2018, 114: 39−67.
19	TAKEICHI N. Nominal flight time optimization for arrival time scheduling through estimation/resolution of delay accumulation. Transportation Research, Part C: Emerging technologies, 2017, 77: 433−443.
20	GU J W, TANG X M, HONG W J, et al Real-time optimization of short-term flight profiles to control time of arrival. Aerospace Science & Technology, 2019, 84, 1164- 1174.
21	NG K K H, LEE C K M, CHAN F T S, et al Robust aircraft sequencing and scheduling problem with arrival/departure delay using the min-max regret approach. Transportation Research Part E: Logistics & Transportation Review, 2017, 106, 115- 136.
22	GENG X, HU M H Simulated annealing method-based flight schedule optimization in multiairport systems. Mathematical Problems in Engineering, 2020, 2020, 4731918.
23	YANG Y C, GAO Z C, HE C Stochastic terminal flight arrival and departure scheduling problem under performance-based navigation environment. Transportation Research Part C: Emerging Technologies, 2020, 119, 102735.
24	HAN Y X, ZHANG J W, HUANG X Q Study of the optimization model for traffic flow. Computers & Industrial Engineering, 2019, 136, 429- 435.
25	ZHAO D L, TAN Y J, CHENG R, et al. The optimal aircraft scheduling model based on network model. Proc. of the 36th Chinese Control Conference, 2017: 2935−2940.
26	HONG Y K, CHOI B H, KIM Y Two-stage stochastic programming based on particle swarm optimization for aircraft sequencing and scheduling. IEEE Trans. on Intelligent Transportation Systems, 2018, 20 (4): 1365- 1377.
27	CHEN X D, YU H, CAO K, et al. Uncertainty-aware flight scheduling for airport throughput and flight delay optimization. IEEE Trans. on Aerospace & Electronic Systems, 2020, 56(2): 853−862.
28	ZHANG L, HUANG Z Y, LIU W, et al Weather radar echo prediction method based on convolution neural network and long short-term memory networks for sustainable e-agriculture. Journal of Cleaner Production, 2021, 298, 126776. doi: 10.1016/j.jclepro.2021.126776
29	FEEYO TECHNOLOGY. Flight status. [2021-01-03]. http://www.variflight.com.
30	MOHAMED R Deep multimodal fusion: a hybrid approach. International Journal of Computer Vision, 2018, 126, 440- 456. doi: 10.1007/s11263-017-0997-7
31	QIN W T, TANG J, LU C, et al Trajectory prediction based on long short-term memory network and Kalman filter using hurricanes as an example. Computational Geosciences, 2021, 25, 1005- 1023. doi: 10.1007/s10596-021-10037-2
32	GREFF K, SRIVASTAVA R K, KOUTNIK J, et al LSTM: a search space odyssey. IEEE Trans. on Neural Networks and Learning Systems, 2017, 28 (10): 2222- 2232. doi: 10.1109/TNNLS.2016.2582924
33	YU Y, SI X S, HU C H, et al A review of recurrent neural networks: LSTM cells and network architectures. Neural Computation, 2019, 31 (7): 1235- 1270. doi: 10.1162/neco_a_01199
34	ZHOU X K, HU Y Y, LIANG W, et al Variational LSTM enhanced anomaly detection for industrial big data. IEEE Trans. on Industrial Informatics, 2021, 17 (5): 3469- 3477. doi: 10.1109/TII.2020.3022432
35	YUAN X H, CHEN C, JIANG M, et al Prediction interval of wind power using parameter optimized Beta distribution based LSTM model. Applied Soft Computing, 2019, 82 (2): 105550.
36	JIN K H, MCCANN M T, FROUSTEY E, et al Deep convolutional neural network for inverse problems in imaging. IEEE Trans. on Image Processing, 2017, 26 (9): 4509- 4522. doi: 10.1109/TIP.2017.2713099
37	SARIGUL M, OZYILDIRIM B M AVC M. Differential convolutional neural network. Neural Networks, 2019, 116, 279- 287. doi: 10.1016/j.neunet.2019.04.025
38	YING X Y, WANG L G, WANG Y Q, et al Deformable 3D convolution for video super-resolution. IEEE Signal Processing Letters, 2020, 27, 1500- 1504. doi: 10.1109/LSP.2020.3013518
39	CHAIB S, LIU H, GU Y F, et al Deep feature fusion for VHR remote sensing scene classification. IEEE Trans. on Geoscience and Remote Sensing, 2017, 55 (8): 4775- 4787. doi: 10.1109/TGRS.2017.2700322
40	TANG J, LIU G, PAN Q T A review on representative swarm intelligence algorithms for solving optimization problems: applications and trends. IEEE/CAA Journal of Automatica Sinica, 2021, 8 (10): 1627- 1643. doi: 10.1109/JAS.2021.1004129

Work	Model framework			Key algorithm	Model execution	Model verification		Year
Work	Stage	Target	Proof	Key algorithm	Model execution	Real data	Compared	Year
[8]	Four	Three	No	Mixed integer linear programming	Dynamic	French airspace	Yes	2020
[9]	Two	Single	No	Shift power law	Dynamic	Delta airlines	No	2019
[10]	Single	Three	Yes	Dantzig-Wolfe decomposition	Static	Iranian airlines	No	2018
[11]	Two	Two	No	Mixed integer linear programming	Static	Chinese airport	Yes	2020
[12]	Two	Two	No	Partheno-genetic algorithm	Static	No	Yes	2017
[13]	Two	Single	No	Mixed integer linear programming	Static	No	Yes	2020
[14]	Three	Two	No	Genetic algorithm	Static	American airline	No	2017
[15]	Single	Single	No	Memetic algorithm	Static	Chinese airway network	Yes	2018
[16]	Two	Single	No	Sample average approximation	Static	Legacy airline company	Yes	2018
[17]	Single	Two	No	Genetic algorithm	Static	Chinese airspace	Yes	2018
[18]	Three	Three	Yes	Mixed integer nonlinear programming	Static	No	Yes	2018
[19]	Single	Single	Yes	Vertical navigation (VNAV) path model	Static	No	No	2017
[20]	Three	Single	No	Genetic algorithm	Dynamic	No	No	2019
[21]	Single	Single	Yes	Artificial bee colony algorithm	Static	No	Yes	2017
[22]	Single	Three	No	Simulated annealing algorithm	Static	Beijing-Tianjin-Hebei airport	Yes	2020
[23]	Single	Single	No	Mixed integer nonlinear programming	Static	Shanghai metroplex	Yes	2020
[24]	Single	Single	No	Max-plus model	Dynamic	No	No	2019
[25]	Single	Single	No	Network model	Static	Chinese airspace	No	2017
[26]	Two	Single	Yes	Particle swarm optimization	Static	Jeju international airport	Yes	2019
[27]	Two	Single	No	Integer linear programming	Static	Eastern China airline	Yes	2019

Start point	End point	August 9th				August 10th				August 11th
Start point	End point	[0,6)	[6,12)	[12,18)	[18,24)	[0,6)	[6,12)	[12,18)	[18,24)	[0,6)	[6,12)	[12,18)	[18,24)
Beijing	Qingdao	(0,0)	(6,0)	(2,0)	(2,2)	(0,0)	(6,0)	(1,1)	(4,2)	(0,0)	(2,7)	(0,2)	(0,8)
	Shanghai	(0,0)	(17,1)	(3,15)	(0,14)	(0,0)	(0,18)	(1,22)	(0,13)	(0,0)	(17,3)	(18,2)	(11,3)
	Hangzhou	(1,0)	(11,0)	(2,4)	(0,8)	(0,1)	(2,9)	(1,7)	(4,4)	(1,0)	(11,1)	(5,1)	(9,0)
	Guangzhou	(0,0)	(13,0)	(13,1)	(4,3)	(0,0)	(9,4)	(13,0)	(7,0)	(0,0)	(12,1)	(13,0)	(7,0)
	Taipei	(0,0)	(2,2)	(1,0)	(1,0)	(0,0)	(2,0)	(2,0)	(1,0)	(0,0)	(2,0)	(1,0)	(2,0)
Qingdao	Beijing	(0,0)	(4,0)	(4,1)	(2,1)	(0,0)	(4,0)	(5,0)	(3,1)	(0,0)	(2,2)	(1,7)	(0,5)
	Shanghai	(0,0)	(8,1)	(6,3)	(0,10)	(0,0)	(0,11)	(0,9)	(0,9)	(0,0)	(6,5)	(2,7)	(2,7)
	Hangzhou	(0,0)	(3,0)	(3,1)	(0,1)	(0,0)	(0,4)	(0,4)	(0,2)	(0,0)	(2,1)	(1,3)	(0,1)
	Guangzhou	(0,0)	(3,0)	(4,0)	(4,0)	(0,0)	(3,0)	(4,0)	(3,2)	(0,0)	(3,0)	(1,3)	(0,5)
	Taipei	(0,0)	(0,1)	(0,2)	(0,0)	(0,0)	(0,0)	(1,0)	(0,0)	(0,0)	(0,0)	(1,0)	(0,0)
Shanghai	Beijing	(0,0)	(18,0)	(13,4)	(1,14)	(0,0)	(1,16)	(0,19)	(0,15)	(0,0)	(8,8)	(13,4)	(14,1)
	Qingdao	(0,0)	(9,2)	(7,1)	(0,11)	(0,0)	(0,11)	(0,8)	(0,10)	(0,0)	(0,12)	(3,4)	(3,7)
	Hangzhou	(0,0)	(0,0)	(0,0)	(0,0)	(0,0)	(0,0)	(0,0)	(0,0)	(0,0)	(0,0)	(0,0)	(0,0)
	Guangzhou	(0,0)	(15,0)	(13,3)	(1,14)	(0,0)	(0,15)	(0,16)	(4,12)	(0,0)	(13,1)	(14,3)	(11,2)
	Taipei	(0,0)	(0,5)	(0,8)	(1,3)	(0,0)	(0,3)	(0,8)	(0,4)	(0,0)	(6,0)	(8,0)	(3,0)
Hangzhou	Beijing	(0,0)	(8,0)	(8,1)	(1,9)	(0,0)	(0,8)	(0,8)	(4,6)	(0,0)	(8,0)	(1,8)	(0,10)
	Qingdao	(0,0)	(5,0)	(0,1)	(1,2)	(0,0)	(0,4)	(0,1)	(0,4)	(0,0)	(1,4)	(0,1)	(2,1)
	Shanghai	(0,0)	(0,0)	(0,0)	(0,0)	(0,0)	(0,0)	(0,0)	(0,0)	(0,0)	(0,0)	(0,0)	(0,0)
	Guangzhou	(2,0)	(7,0)	(9,1)	(1,8)	(0,2)	(0,8)	(0,9)	(2,7)	(1,1)	(5,3)	(9,1)	(8,0)
	Taipei	(0,0)	(0,1)	(0,1)	(1,0)	(0,0)	(0,2)	(0,0)	(1,1)	(0,0)	(2,0)	(0,0)	(1,0)
Guangzhou	Beijing	(0,0)	(8,0)	(9,0)	(8,3)	(0,0)	(7,1)	(11,2)	(11,0)	(0,0)	(8,0)	(13,0)	(10,1)
	Qingdao	(0,0)	(1,4)	(5,0)	(2,0)	(0,0)	(4,0)	(4,1)	(2,1)	(0,0)	(0,4)	(2,3)	(2,1)
	Shanghai	(1,1)	(17,0)	(6,11)	(0,10)	(0,2)	(0,17)	(2,15)	(5,7)	(4,1)	(13,9)	(16,1)	(9,1)
	Hangzhou	(2,0)	(9,0)	(3,5)	(0,9)	(0,2)	(0,9)	(1,8)	(6,4)	(1,1)	(6,4)	(8,0)	(8,0)
	Taipei	(0,0)	(0,1)	(2,1)	(0,0)	(0,0)	(1,0)	(2,0)	(1,0)	(0,0)	(1,0)	(2,0)	(0,0)
Taipei	Beijing	(0,0)	(1,0)	(1,2)	(1,0)	(0,0)	(1,0)	(2,0)	(2,0)	(0,0)	(0,0)	(3,0)	(2,0)
	Qingdao	(0,0)	(0,1)	(0,2)	(0,0)	(0,0)	(1,0)	(0,0)	(0,0)	(0,0)	(1,0)	(0,0)	(0,0)
	Shanghai	(0,0)	(0,4)	(1,11)	(0,0)	(0,0)	(0,2)	(0,2)	(0,1)	(0,0)	(6,0)	(12,0)	(1,0)
	Hangzhou	(0,0)	(0,0)	(1,1)	(0,1)	(0,0)	(0,1)	(0,1)	(1,1)	(0,0)	(1,0)	(2,0)	(0,0)
	Guangzhou	(0,0)	(0,1)	(3,1)	(0,0)	(0,0)	(0,0)	(3,0)	(1,0)	(0,0)	(1,0)	(2,0)	(0,0)

Airport	Location
Beijing Capital Airport	（40.08 N，116.58 E）
Beijing Daxing Airport	（39.51 N，116.41 E）
Qingdao Liuting Airport	（36.27 N，120.38 E）
Shanghai Hongqiao Airport	（31.19 N，121.34 E）
Shanghai Pudong Airport	（31.14 N，121.79 E）
Hangzhou Xiaoshan Airport	（30.33 N，120.22 E）
Guangzhou Baiyun Airport	（23.18 N，113.26 E）
Taipei Taoyuan Airport	（25.09 N，121.60 E）
Taipei Songshan Airport	（25.06 N，121.55 E）

Date	Time	Location
August 9th	0:00	(26.5 N, 123.4 E)
	6:00	(27.0 N, 122.5 E)
	12:00	(27.5 N, 122.0 E)
	18:00	(28.3 N, 121.4 E)
August 10th	0:00	(28.9 N, 120.8 E)
	6:00	(29.9 N, 120.3 E)
	12:00	(30.7 N, 120.2 E)
	18:00	(31.7 N, 120.5 E)
August 11th	0:00	(33.6 N, 120.2 E)
	6:00	(34.8 N, 119.9 E)
	12:00	(35.8 N, 120.2 E)
	18:00	(36.9 N, 119.7 E)

Start point	End point	August 9th				August 10th				August 11th
Start point	End point	[0,6)	[6,12)	[12,18)	[18,24)	[0,6)	[6,12)	[12,18)	[18,24)	[0,6)	[6,12)	[12,18)	[18,24)
Beijing	Qingdao	(0,0)	(6,0)	(2,0)	(2,2)	(0,0)	(6,0)	(1,1)	(4,2)	(0,0)	(2,7)	(0,2)	(0,8)
	Shanghai	(0,0)	(17,1)	(3,15)	(0,14)	(0,0)	(0,18)	(1,22)	(0,13)	(0,0)	(17,3)	(18,2)	(11,3)
	Hangzhou	(1,0)	(11,0)	(2,4)	(0,8)	(0,1)	(2,9)	(1,7)	(4,4)	(1,0)	(11,1)	(5,1)	(9,0)
	Guangzhou	(0,0)	(13,0)	(13,1)	(4,3)	(0,0)	(9,4)	(13,0)	(7,0)	(0,0)	(12,1)	(13,0)	(7,0)
	Taipei	(0,0)	(2,2)	(1,0)	(1,0)	(0,0)	(2,0)	(2,0)	(1,0)	(0,0)	(2,0)	(1,0)	(2,0)
Qingdao	Beijing	(0,0)	(4,0)	(4,1)	(2,1)	(0,0)	(4,0)	(5,0)	(3,1)	(0,0)	(2,2)	(1,7)	(0,5)
	Shanghai	(0,0)	(8,1)	(6,3)	(0,10)	(0,0)	(0,11)	(0,9)	(0,9)	(0,0)	(6,5)	(2,7)	(2,7)
	Hangzhou	(0,0)	(3,0)	(3,1)	(0,1)	(0,0)	(0,4)	(0,4)	(0,2)	(0,0)	(2,1)	(1,3)	(0,1)
	Guangzhou	(0,0)	(3,0)	(4,0)	(4,0)	(0,0)	(3,0)	(4,0)	(3,2)	(0,0)	(3,0)	(1,3)	(0,5)
	Taipei	(0,0)	(0,1)	(0,2)	(0,0)	(0,0)	(0,0)	(1,0)	(0,0)	(0,0)	(0,0)	(1,0)	(0,0)
Shanghai	Beijing	(0,0)	(18,0)	(13,4)	(1,14)	(0,0)	(1,16)	(0,19)	(0,15)	(0,0)	(8,8)	(13,4)	(14,1)
	Qingdao	(0,0)	(9,2)	(7,1)	(0,11)	(0,0)	(0,11)	(0,8)	(0,10)	(0,0)	(0,12)	(3,4)	(3,7)
	Hangzhou	(0,0)	(0,0)	(0,0)	(0,0)	(0,0)	(0,0)	(0,0)	(0,0)	(0,0)	(0,0)	(0,0)	(0,0)
	Guangzhou	(0,0)	(15,0)	(13,3)	(1,14)	(0,0)	(0,15)	(0,16)	(4,12)	(0,0)	(13,1)	(14,3)	(11,2)
	Taipei	(0,0)	(0,5)	(0,8)	(1,3)	(0,0)	(0,3)	(0,8)	(0,4)	(0,0)	(6,0)	(8,0)	(3,0)
Hangzhou	Beijing	(0,0)	(8,0)	(8,1)	(1,9)	(0,0)	(0,8)	(0,8)	(4,6)	(0,0)	(8,0)	(1,8)	(0,10)
	Qingdao	(0,0)	(5,0)	(0,1)	(1,2)	(0,0)	(0,4)	(0,1)	(0,4)	(0,0)	(1,4)	(0,1)	(2,1)
	Shanghai	(0,0)	(0,0)	(0,0)	(0,0)	(0,0)	(0,0)	(0,0)	(0,0)	(0,0)	(0,0)	(0,0)	(0,0)
	Guangzhou	(2,0)	(7,0)	(9,1)	(1,8)	(0,2)	(0,8)	(0,9)	(2,7)	(1,1)	(5,3)	(9,1)	(8,0)
	Taipei	(0,0)	(0,1)	(0,1)	(1,0)	(0,0)	(0,2)	(0,0)	(1,1)	(0,0)	(2,0)	(0,0)	(1,0)
Guangzhou	Beijing	(0,0)	(8,0)	(9,0)	(8,3)	(0,0)	(7,1)	(11,2)	(11,0)	(0,0)	(8,0)	(13,0)	(10,1)
	Qingdao	(0,0)	(1,4)	(5,0)	(2,0)	(0,0)	(4,0)	(4,1)	(2,1)	(0,0)	(0,4)	(2,3)	(2,1)
	Shanghai	(1,1)	(17,0)	(6,11)	(0,10)	(0,2)	(0,17)	(2,15)	(5,7)	(4,1)	(13,9)	(16,1)	(9,1)
	Hangzhou	(2,0)	(9,0)	(3,5)	(0,9)	(0,2)	(0,9)	(1,8)	(6,4)	(1,1)	(6,4)	(8,0)	(8,0)
	Taipei	(0,0)	(0,1)	(2,1)	(0,0)	(0,0)	(1,0)	(2,0)	(1,0)	(0,0)	(1,0)	(2,0)	(0,0)
Taipei	Beijing	(0,0)	(1,0)	(1,2)	(1,0)	(0,0)	(1,0)	(2,0)	(2,0)	(0,0)	(0,0)	(3,0)	(2,0)
	Qingdao	(0,0)	(0,1)	(0,2)	(0,0)	(0,0)	(1,0)	(0,0)	(0,0)	(0,0)	(1,0)	(0,0)	(0,0)
	Shanghai	(0,0)	(0,4)	(1,11)	(0,0)	(0,0)	(0,2)	(0,2)	(0,1)	(0,0)	(6,0)	(12,0)	(1,0)
	Hangzhou	(0,0)	(0,0)	(1,1)	(0,1)	(0,0)	(0,1)	(0,1)	(1,1)	(0,0)	(1,0)	(2,0)	(0,0)
	Guangzhou	(0,0)	(0,1)	(3,1)	(0,0)	(0,0)	(0,0)	(3,0)	(1,0)	(0,0)	(1,0)	(2,0)	(0,0)

A deep multimodal fusion and multitasking trajectory prediction model for typhoon trajectory prediction to reduce flight scheduling cancellation

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Date	Time	Predicted trajectory location
August 9th	0:00	(26.658 N, 124.986 E)
	6:00	(27.007 N, 123.023 E)
	12:00	(27.905 N, 121.832 E)
	18:00	(28.424 N, 121.794 E)
August 10th	0:00	(29.593 N, 121.144 E)
	6:00	(28.627 N, 119.829 E)
	12:00	(30.038 N, 1196.68 E)
	18:00	(307.72 N, 120.367 E)
August 11th	0:00	(321.87 N, 120.733 E)
	6:00	(34.672 N, 120.636 E)
	12:00	(35.968 N, 119.747 E)
	18:00	(374.26 N, 119.390 E)