Application of Convolutional Neural Networks (CNN) for Optimizing Route Changes Based on Dynamic Weather Conditions and Travel Time

Sunardi Sunardi, Marzuki Sinambela, Syahrul Humaidi, Marhaposan Situmorang

Abstract


This research aims to apply Convolutional Neural Networks (CNN) in optimizing flight route changes by considering dynamic weather conditions and travel time. In the context of air traffic management, fast and precise route changes are critical to reducing flight delays and increasing operational efficiency. The data used includes flight information, current weather conditions and historical travel time data. The research process begins with data collection and preprocessing to ensure the quality and consistency of the information. The CNN model is built with an architecture consisting of several convolution and pooling layers to extract features from input data. The evaluation results show that the CNN model achieves an accuracy of 92% in predicting optimal route changes, which shows better performance compared to traditional algorithms such as support vector machine and random forest. These findings confirm the potential of CNNs in improving air traffic management through route change optimization based on real-time analysis of weather and time data. This research contributes to the development of a more adaptive and responsive system in facing challenges in the aviation industry.

Keywords


Prediction, CNN, Optimization, air traffic, weather.

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References


V. M. Do, Q. H. Tran, K. G. Le, X. C. Vuong, and V. T. Vu, “Enhanced Deep Neural Networks for Traffic Speed Forecasting Regarding Sustainable Traffic Management Using Probe Data from Registered Transport Vehicles on Multilane Roads,” Sustainability, vol. 16, no. 6, p. 2453, 2024.

A. G. Ismaeel et al., “Traffic pattern classification in smart cities using deep recurrent neural network,” Sustainability, vol. 15, no. 19, p. 14522, 2023.

S. Ryu, D. Kim, and J. Kim, “Weather-aware long-range traffic forecast using multi-module deep neural network,” Appl. Sci., vol. 10, no. 6, p. 1938, 2020.

S. Malik and D. Kim, “Optimal travel route recommendation mechanism based on neural networks and particle swarm optimization for efficient tourism using tourist vehicular data,” Sustainability, vol. 11, no. 12, p. 3357, 2019.

S. M. Abdullah et al., “Optimizing traffic flow in smart cities: Soft GRU-based recurrent neural networks for enhanced congestion prediction using deep learning,” Sustainability, vol. 15, no. 7, p. 5949, 2023.

E. Ukwandu et al., “Cyber-security challenges in aviation industry: A review of current and future trends,” Information, vol. 13, no. 3, p. 146, 2022.

A. Jiménez-Crisóstomo, L. Rubio-Andrada, M. S. Celemín-Pedroche, and M. Escat-Cortés, “The constrained air transport energy paradigm in 2021,” Sustainability, vol. 13, no. 5, p. 2830, 2021.

Afis Julianto, Andi Sunyoto, and Ferry Wahyu Wibowo, “Optimasi Hyperparameter Convolutional Neural Network Untuk Klasifikasi Penyakit Tanaman Padi,” Tek. Teknol. Inf. dan Multimed., vol. 3, no. 2, pp. 98–105, 2022, doi: 10.46764/teknimedia.v3i2.77.

P. A. Nugroho, I. Fenriana, and R. Arijanto, “Implementasi Deep Learning Menggunakan Convolutional Neural Network ( Cnn ) Pada Ekspresi Manusia,” Algor, vol. 2, no. 1, pp. 12–21, 2020.

M. Arfah, F. Fachrizal, and O. Nugroho, “DEVELOPING A MODEL OF ASSOCIATION RULES WITH MACHINE LEARNING IN DETERMINING USER HABITS ON SOCIAL MEDIA.,” Eastern-European J. Enterp. Technol., no. 2, 2024.

A. R. Lubis, H. R. Safitri, M. Lubis, and O. Nugroho, “Implementation of Preprocessing in Text Summarization Techniques for Indonesian Language Documents Using the Flax T5 Approach,” in 2023 11th International Conference on Cyber and IT Service Management (CITSM), IEEE, 2023, pp. 1–6.

tri andre anu Rahmatika, Ahmad, okvi nugroho, alkhowarizmi, “USING RELATIONAL LEARNING IN EXPLORING THE EFFECTIVENESS OF USING HASHTAGS IN FUTURE TOPICS AND USER RELATIONS IN X,” vol. 2, pp. 62–68, 2024, doi: 10.15587/1729-4061.2024.306726.

R. Dalmau, “Probabilistic and explainable tree-based models for rotational reactionary flight delay prediction,” CEAS Aeronaut. J., pp. 1–17, 2024.

Y. Ai, W. Pan, C. Yang, D. Wu, and J. Tang, “A deep learning approach to predict the spatial and temporal distribution of flight delay in network,” J. Intell. Fuzzy Syst., vol. 37, no. 5, pp. 6029–6037, 2019.

Ü. Çelik and H. Eren, “Classification of manifold learning based flight fingerprints of UAVs in air traffic,” IEEE Trans. Intell. Transp. Syst., 2023.

S. A. Rather and P. S. Bala, “Lévy flight and chaos theory based gravitational search algorithm for multilayer perceptron training,” Evol. Syst., vol. 14, no. 3, pp. 365–392, 2023.

W. Shao, A. Prabowo, S. Zhao, P. Koniusz, and F. D. Salim, “Predicting flight delay with spatio-temporal trajectory convolutional network and airport situational awareness map,” Neurocomputing, vol. 472, pp. 280–293, 2022.

A. R. Lubis, M. K. M. Nasution, O. S. Sitompul, and E. M. Zamzami, “A Framework of Utilizing Big Data of Social Media to Find Out the Habits of Users Using Keyword,” 2020, pp. 140–144.

M. E. Al Khowarizmi, Rahmad Syah, Mahyuddin K. M. Nasution, “Sensitivity of MAPE using detection rate for big data forecasting crude palm oil on k-nearest neighbor,” Int. J. Electr. Comput. Eng., vol. 11, no. 3, pp. 2697–2704, 2021, doi: 10.11591/ijece.v11i3.pp2697-2704.

O. Nugroho, O. S. Sitompul, and S. Suherman, “Identification of Regional Origin Based on Dialec Using the Perceptron Evolving Multilayer Method,” J. MEDIA Inform. BUDIDARMA, vol. 7, no. 3, pp. 1613–1621, 2023.

Y. J. Kim, S. Choi, S. Briceno, and D. Mavris, “A deep learning approach to flight delay prediction,” in 2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC), IEEE, 2016, pp. 1–6.

B. Yu, Z. Guo, S. Asian, H. Wang, and G. Chen, “Flight delay prediction for commercial air transport: A deep learning approach,” Transp. Res. Part E Logist. Transp. Rev., vol. 125, pp. 203–221, 2019.

P. N. Kasabov, “Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines,” IEEE Trans. Neural Networks, vol. 16, no. 1, pp. 286–286, 2005, doi: 10.1109/tnn.2004.842676.

Z. Wang, C. Liao, X. Hang, L. Li, D. Delahaye, and M. Hansen, “Distribution Prediction of Strategic Flight Delays via Machine Learning Methods,” Sustainability, vol. 14, no. 22, p. 15180, 2022.

R. S. Ganesh, “Prediction based on social media dataset using CNN-LSTM to classify the accurate Aggression level,” in 2021 International Conference on Computer Communication and Informatics (ICCCI), IEEE, 2021, pp. 1–4.

D. Alghazzawi, O. Bamasag, A. Albeshri, I. Sana, H. Ullah, and M. Z. Asghar, “Efficient prediction of court judgments using an LSTM+ CNN neural network model with an optimal feature set,” Mathematics, vol. 10, no. 5, p. 683, 2022.


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