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Fusion: Practice and Applications
Volume 14 , Issue 1, PP: 283-292 , 2024 | Cite this article as | XML | Html |PDF

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An Information Fusion Technique for Prognosticating Future Air Passenger Trends

  Luis A. Zambrano 1 * ,   Luis llerena Ocaña 2 ,   Tannia Cristina P. Morales 3 ,   Vladimir Vega Falcón 4 ,   Mirzaliev Sanjar 5

1  Universidad Regional Autónoma de los Andes (UNIANDES), Ecuador
    (uq.luisalbarracin@uniandes.edu.ec)

2  Universidad Regional Autónoma de los Andes (UNIANDES), Ecuador
    (ua.luisllerena@uniandes.edu.ec)

3  Universidad Regional Autónoma de los Andes (UNIANDES), Ecuador
    (ua.tanniapoveda@uniandes.edu.ec)

4  International Center for Entrepreneurs in Barcelona (ICEB), Spain
    (vega.vladimir@gmail.com)

5  TSUE Research department, Uzbekistan
    (s.mirzaliev@tsue.uz)

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Doi   :   https://doi.org/10.54216/FPA.140121

Received: June 29, 2023 Revised: October 18, 2023 Accepted: December 17, 2023

Abstract :

The aviation industry is constantly changing and to keep up with the trends of air passengers we need predictive models. In this paper, we explore the use of Information Fusion methodologies and classical time series techniques to forecast how many passengers will be traveling by air. Predicting passenger demands is a task, due to various factors that influence travel patterns. The existing models often struggle to capture the dynamics in this field so it's crucial to develop accurate forecasting methods. By leveraging information fusion techniques like smoothing and Autoregressive Integrated Moving Average (ARIMA) our research creates models based on historical data of air passenger volumes. These techniques combine machine learning algorithms and time series analysis to identify dependencies and patterns in the dataset. Through evaluations and comparative analyses, our proposed models demonstrate promising capabilities in forecasting future air passenger volumes. Proof-of-concept experiments based on 5-fold cross-validation demonstrate the efficacy of the proposed approach in capturing underlying trends and seasonality within the dataset.

Keywords :

Information Fusion; Air Passenger Forecasting , Predictive Analysis; Machine Learning; Time Series Prediction; Aviation Industry Trends; Exponential Smoothing; ARIMA Modeling; Transportation Forecasting.

References :

[1]    Siddiqui, Shahid S, Sivakumar Loganathan, Venkateswaran R Elangovan, and M Yusuf Ali. 2023. “Artificial Intelligence in Precision Medicine.” In A Handbook of Artificial Intelligence in Drug Delivery, 531–69. Elsevier.

[2]    Khalid, Salman, Jinwoo Song, Muhammad Muzammil Azad, Muhammad Umar Elahi, Jaehun Lee, Soo-Ho Jo, and Heung Soo Kim. 2023. “A Comprehensive Review of Emerging Trends in Aircraft Structural Prognostics and Health Management.” Mathematics 11 (18): 3837.

[3]    Nguyen, Thanh Thi, Quoc Viet Hung Nguyen, Dung Tien Nguyen, Samuel Yang, Peter W Eklund, Thien Huynh-The, Thanh Tam Nguyen, Quoc-Viet Pham, Imran Razzak, and Edbert B Hsu. 2020. “Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions.” ArXiv Preprint ArXiv:2008.07343.

[4]    Jin, Byoungsam, and Youngchul Bae. 2023. “Prospective Research Trend Analysis on Zero-Energy Building (ZEB): An Artificial Intelligence Approach.” Sustainability 15 (18): 13577.

[5]    Muthuswamy, M., & M. Ali, A. (2023). Sustainable Supply Chain Management in the Age of Machine Intelligence: Addressing Challenges, Capitalizing on Opportunities, and Shaping the Future Landscape. Sustainable Machine Intelligence Journal, 3. https://doi.org/10.61185/SMIJ.2023.33103

[6]    Chee, Marcel Lucas, Mark Leonard Chee, Haotian Huang, Katelyn Mazzochi, Kieran Taylor, Han Wang, Mengling Feng, et al. 2023. “Artificial Intelligence and Machine Learning in Prehospital Emergency Care: A Scoping Review.” Iscience.

[7]    Chadaga, Krishnaraj, Srikanth Prabhu, Niranjana Sampathila, Sumith Nireshwalya, Swathi S Katta, Ru-San Tan, and U Rajendra Acharya. 2023. “Application of Artificial Intelligence Techniques for Monkeypox: A Systematic Review.” Diagnostics 13 (5): 824.

[8]    Badgujar, Chetan, Sanjoy Das, Dania Martinez Figueroa, and Daniel Flippo. 2023. “Application of Computational Intelligence Methods in Agricultural Soil--Machine Interaction: A Review.” Agriculture 13 (2): 357.

[9]    Chen, Lin, Zhonghao Chen, Yubing Zhang, Yunfei Liu, Ahmed I Osman, Mohamed Farghali, Jianmin Hua, et al. 2023. “Artificial Intelligence-Based Solutions for Climate Change: A Review.” Environmental Chemistry Letters, 1–33.

[10] Davenport, Thomas H, and Nitin Mittal. 2023. All-in on AI: How Smart Companies Win Big with Artificial Intelligence. Harvard Business Press.

[11] Miya, Javed, Sandeep Raj, M A Ansari, Suresh Kumar, and Ranjit Kumar. 2023. “Artificial Intelligence Advancement for 6G Communication: A Visionary Approach.” In 6G Enabled Fog Computing in IoT: Applications and Opportunities, 355–94. Springer.

[12] He, Haonan, Liangyu Chen, and Shanyong Wang. 2023. “Flight Short-Term Booking Demand Forecasting Based on a Long Short-Term Memory Network.” Computers \& Industrial Engineering 186: 109707.

[13] Yuan, Yuan, Fengchen Fu, Yaling Li, Yao Xing, Lei Wang, Hao Zheng, and Wei Ye. 2023. “Research and Application of Intelligent Weather Push Model Based on Travel Forecast and 5G Message.” Atmosphere 14 (11): 1658.

[14] Chee, Marcel Lucas, Mark Leonard Chee, Haotian Huang, Katie Mazzochi, Kieran Taylor, Han Wang, Mengling Feng, et al. 2023. “Artificial Intelligence and Machine Learning in Prehospital Emergency Care: A Systematic Scoping Review.” MedRxiv, 2004–23.

[15] Haenlein, Michael, and Andreas Kaplan. 2019. “A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence.” California Management Review 61 (4): 5–14.

[16] Moravec, Hans P. 1999. Robot: Mere Machine to Transcendent Mind. Oxford University Press.

[17] Imran, Ali, Ahmed Zoha, and Adnan Abu-Dayya. 2014. “Challenges in 5G: How to Empower SON with Big Data for Enabling 5G.” IEEE Network 28 (6): 27–33.

[18] Kothai, G, E Poovammal, Gaurav Dhiman, Kadiyala Ramana, Ashutosh Sharma, Mohammed A AlZain, Gurjot Singh Gaba, and Mehedi Masud. 2021. “A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities.” Wireless Communications and Mobile Computing 2021: 1–13.

[19] Pugliano, John. 2017. The Robots Are Coming: A Human’s Survival Guide to Profiting in the Age of Automation. Simon and Schuster.

[20] Le, Dy D, Vung Pham, Huyen N Nguyen, and Tommy Dang. 2019. “Visualization and Explainable Machine Learning for Efficient Manufacturing and System Operations.” Smart and Sustainable Manufacturing Systems 3 (2): 127–47.

[21] Miller, Jim, Jon Patterson, and Ray Garbos. 2013. “Wireless Power and Data System for Integrated System Health Management of Systems Operating in the Harsh Environment of Deep Space.” In Annual Conference of the PHM Society. Vol. 5.

[22] Thakuriah, Piyushimita, D Glenn Geers, and others. 2013. Transportation and Information: Trends in Technology and Policy. Springer.

[23] Allioui, Hanane, and Youssef Mourdi. 2023. “Exploring the Full Potentials of IoT for Better Financial Growth and Stability: A Comprehensive Survey.” Sensors 23 (19): 8015.

[24] Li, Jiwen, Jiapeng Dai, Alibek Issakhov, Sattam Fahad Almojil, and Alireza Souri. 2021. “Towards Decision Support Systems for Energy Management in the Smart Industry and Internet of Things.” Computers \& Industrial Engineering 161: 107671.

Cite this Article as :
Style #
MLA Luis A. Zambrano, Luis llerena Ocaña, Tannia Cristina P. Morales, Vladimir Vega Falcón, Mirzaliev Sanjar. "An Information Fusion Technique for Prognosticating Future Air Passenger Trends." Fusion: Practice and Applications, Vol. 14, No. 1, 2024 ,PP. 283-292 (Doi   :  https://doi.org/10.54216/FPA.140121)
APA Luis A. Zambrano, Luis llerena Ocaña, Tannia Cristina P. Morales, Vladimir Vega Falcón, Mirzaliev Sanjar. (2024). An Information Fusion Technique for Prognosticating Future Air Passenger Trends. Journal of Fusion: Practice and Applications, 14 ( 1 ), 283-292 (Doi   :  https://doi.org/10.54216/FPA.140121)
Chicago Luis A. Zambrano, Luis llerena Ocaña, Tannia Cristina P. Morales, Vladimir Vega Falcón, Mirzaliev Sanjar. "An Information Fusion Technique for Prognosticating Future Air Passenger Trends." Journal of Fusion: Practice and Applications, 14 no. 1 (2024): 283-292 (Doi   :  https://doi.org/10.54216/FPA.140121)
Harvard Luis A. Zambrano, Luis llerena Ocaña, Tannia Cristina P. Morales, Vladimir Vega Falcón, Mirzaliev Sanjar. (2024). An Information Fusion Technique for Prognosticating Future Air Passenger Trends. Journal of Fusion: Practice and Applications, 14 ( 1 ), 283-292 (Doi   :  https://doi.org/10.54216/FPA.140121)
Vancouver Luis A. Zambrano, Luis llerena Ocaña, Tannia Cristina P. Morales, Vladimir Vega Falcón, Mirzaliev Sanjar. An Information Fusion Technique for Prognosticating Future Air Passenger Trends. Journal of Fusion: Practice and Applications, (2024); 14 ( 1 ): 283-292 (Doi   :  https://doi.org/10.54216/FPA.140121)
IEEE Luis A. Zambrano, Luis llerena Ocaña, Tannia Cristina P. Morales, Vladimir Vega Falcón, Mirzaliev Sanjar, An Information Fusion Technique for Prognosticating Future Air Passenger Trends, Journal of Fusion: Practice and Applications, Vol. 14 , No. 1 , (2024) : 283-292 (Doi   :  https://doi.org/10.54216/FPA.140121)
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