GIS-Based Aviation Route Optimization Using Artificial Intelligence: A Deep Learning Approach for Airspace Management
Keywords:
Geographic Information System; Artificial Intelligence; Aviation Route Optimization; Deep Learning; Air Traffic Management; Airspace Management; LSTM; CNN.Abstract
The integration of Geographic Information Systems (GIS) with Artificial Intelligence (AI) presents transformative opportunities for aviation route optimization and airspace management. This study proposes a deep learning-based framework that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks with GIS spatial data to optimize aircraft routing and predict air traffic congestion over Indonesian airspace. The proposed system leverages multi-source geospatial datasets including terrain elevation models, meteorological data, restricted airspace zones, and historical flight trajectories to generate dynamically optimized routes. Experimental results demonstrate that the AI-GIS integrated framework achieves a 23.4% reduction in route deviations, a 17.8% improvement in fuel efficiency, and a 31.2% decrease in conflict detection response time compared to conventional air traffic management approaches. The system was evaluated using data from Kualanamu International Airport (KNO) and surrounding airspace in Sumatera, Indonesia. This research contributes to the advancement of intelligent aviation systems and provides a replicable methodology applicable to regional airspace management in developing countries.References
[1] P. Jia, H. Chen, L. Zhang, and D. Han, "Attention-LSTM based prediction model for aircraft 4-D trajectory," Scientific Reports, vol. 12, no. 1, p. 15533, Sep. 2022. doi: 10.1038/s41598-022-19794-
[2] W. Zeng, Z. Quan, Z. Zhao, C. Xie, and X. Lu, "A deep learning approach for aircraft trajectory prediction in terminal airspace," IEEE Access, vol. 8, pp. 151250–151266, Aug. 2020. doi: 10.1109/ACCESS.2020.3016289
[3] Z. Shi, M. Xu, and Q. Pan, "4-D flight trajectory prediction with constrained LSTM network," IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 11, pp. 7242–7255, Nov. 2021. doi: 10.1109/TITS.2020.3004807
[4] Q. Xu, Y. Pang, X. Zhou, and Y. Liu, "PIGAT: Physics-informed graph attention transformer for air traffic state prediction," IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 9, pp. 12561–12577, Sep. 2024. doi: 10.1109/TITS.2024.3382589
[5] Q. Xu, Y. Pang, and Y. Liu, "Dynamic airspace sectorization with machine learning enhanced workload prediction and clustering," Journal of Air Transport Management, vol. 121, p. 102683, Dec. 2024. doi: 10.1016/j.jairtraman.2024.102683
[6] L. Yang et al., "Multi-step prediction of aircraft trajectory based on CNN-LSTM neural network," Electronics, vol. 11, no. 21, p. 3453, Oct. 2022. doi: 10.3390/electronics11213453
[7] D. Sui and K. Zhang, "A tactical conflict detection and resolution method for en route conflicts in trajectory-based operations," Journal of Advanced Transportation, vol. 2022, pp. 1–16, 2022. doi: 10.1155/2022/9325097
[8] K. Kim, R. Deshmukh, and I. Hwang, "Development of data-driven conflict resolution generator for en-route airspace," Aerospace Science and Technology, vol. 114, p. 106744, Jul. 2021. doi: 10.1016/j.ast.2021.106744
[9] Z. Wang, H. Li, J. Wang, and F. Shen, "Tactical conflict solver assisting air traffic controllers using deep reinforcement learning," Aerospace, vol. 10, no. 2, p. 182, Feb. 2023. doi: 10.3390/aerospace10020182
[10] N. Schimpf et al., "A generalized approach to aircraft trajectory prediction via supervised deep learning," IEEE Access, vol. 11, pp. 114142–114156, Oct. 2023. doi: 10.1109/ACCESS.2023.3325053
[11] R. Alligier, D. Gianazza, and N. Durand, "Machine learning and mass estimation methods for ground-based aircraft climb prediction," IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 6, pp. 3138–3149, Dec. 2015. doi: 10.1109/TITS.2015.2437452
[12] A. Subramanian and S. Mahadevan, "Identifying transient and persistent errors in aircraft cruise trajectory prediction using Bayesian state estimation," Transportation Research Part C: Emerging Technologies, vol. 139, p. 103665, Jun. 2022. doi: 10.1016/j.trc.2022.103665
[13] S. Murca and J. Clarke, "Identification and prediction of urban airspace availability for emerging air mobility operations," Transportation Research Part C: Emerging Technologies, vol. 131, p. 103274, Oct. 2021. doi: 10.1016/j.trc.2021.103274
[14] M. Gariel, A. N. Srivastava, and E. Feron, "Trajectory clustering and an application to airspace monitoring," IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 4, pp. 1511–1524, Dec. 2011. doi: 10.1109/TITS.2011.2160628
[15] J. Brittain and P. Wei, "Scalable autonomous separation assurance with heterogeneous multi-agent reinforcement learning," IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 8, pp. 12474–12483, Aug. 2022. doi: 10.1109/TITS.2021.3114388
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Donny Sanjaya, Ramadhanu Ginting (Author)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
