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()   

  • Received:2022-05-17 Online:2024-06-18 Published:2024-06-19
  • Contact: Jun TANG;;;
  • 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:

    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:

    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:

    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:
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (62073330).


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