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Trends and challenges of forecasting in the airline industry research

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Języki publikacji
EN
Abstrakty
EN
This study aims to comprehensively review aviation forecasting research by identifying its bibliometric trends, evolving research areas, and thematic developments. It focuses on understanding the aviation industry’s research gaps, highlighting emerging trends, and offering insights into future forecasting innovations. A systematic literature review in the Scopus database used Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) and bibliometric analysis. It identified key patterns, influential publications, and emerging topics. A science mapping analysis was executed to pinpoint research trends in airline forecasting using Biblioshiny to visualise the network analysis and thematic evolution keywords mapping. The study categorised research trends and identified underexplored areas for future investigation. The findings reveal significant shifts in aviation forecasting research, with three distinct phases of publication growth and a surge in output from 2016 onwards. Passenger demand forecasting remains the most researched topic, though its growth has stabilised. Emerging issues such as customer behaviour, financial forecasting, and dynamic pricing have gained prominence, driven by advancements in machine learning and big data analytics. The study also highlights transitioning from traditional statistical methods to more advanced predictive techniques, emphasising real-time decision-making and operational efficiency. Established research areas, such as air cargo forecasting and f leet scheduling, have become more standardised, reducing the need for further innovation.
Rocznik
Strony
23--36
Opis fizyczny
Bibliogr. 53 poz., tab., wykr.
Twórcy
  • Mechanical and Industrial Engineering Department, Universitas Gadjah Mada, Grafika Street No. 2, Sinduadi, Mlati District, Sleman Regency, Special Region of Yogyakarta, Indonesia
  • Universitas Internasional Semen Indonesia PT Semen Indonesia Complex, Veteran Street, Gresik, East Java, Indonesia
  • Mechanical and Industrial Engineering Department, Universitas Gadjah Mada, Grafika Street No. 2, Sinduadi, Mlati District, Sleman Regency, Special Region of Yogyakarta, Indonesia
  • Mechanical and Industrial Engineering Department, Universitas Gadjah Mada, Grafika Street No. 2, Sinduadi, Mlati District, Sleman Regency, Special Region of Yogyakarta, Indonesia
Bibliografia
  • Abdella, J. A., Zaki, N. M., Shuaib, K., & Khan, F. (2021). Airline ticket price and demand prediction: A survey. Journal of King Saud University - Computer and Information Sciences, 33(4), 375-391. doi: 10.1016/j.jksuci.2019.02.001
  • Andrés, E., González-Arribas, D., Soler, M., Kamgarpour, M., & Sanjurjo-Rivo, M. (2021). Informed scenariobased RRT∗ for aircraft trajectory planning under ensemble forecasting of thunderstorms. Transportation Research Part C: Emerging Technologies, 129. doi: 10.1016/j.trc.2021.103232
  • Asif, S., Rafi, R., & Ali, A. (2020). A bibliometric analysis of revenue management in airline industry. Journal of Revenue and Pricing Management, 2002. doi: 10.1057/s41272-020-00247-1
  • Banerjee, N., Morton, A., & Akartunalı, K. (2020). Passenger demand forecasting in scheduled transportation.European Journal of Operational Research, 286(3), 797-810. doi: 10.1016/j.ejor.2019.10.032
  • Baptista, M., Sankararaman, S., de Medeiros, I. P., Nascimento, C., Prendinger, H., & Henriques, E. M. P. (2018). Forecasting fault events for predictive maintenance using data-driven techniques and ARMA modeling. Computers and Industrial Engineering, 115, 41-53. doi: 10.1016/j.cie.2017.10.033
  • Caiado, J., & Lúcio, F. (2023). Stock market forecasting accuracy of asymmetric GARCH models during the COVID-19 pandemic. North American Journal of Economics and Finance, 68, 101971. doi: 10.1016/j.najef.2023.101971
  • Choi, S., Kim, Y. J., Briceno, S., & Mavris, D. (2016). Prediction of weather-induced airline delays based on machine learning algorithms. 2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC), 1-6. doi: 10.1109/DASC.2016.7777956.
  • Colizza, V., Barrat, A., Barthélemy, M., & Vespignani, A. (2006). The role of the airline transportation network in the prediction and predictability of global epidemics. Proceedings of the National Academy of Sciences of the United States of America, 103(7), 20152020. doi: 10.1073/pnas.0510525103
  • Czerny, A. I., Fu, X., Lei, Z., & Oum, T. H. (2021). Post pandemic aviation market recovery: Experience and lessons from China. Journal of Air Transport Management, 90, 101971. doi: 10.1016/j.jairtraman.2020.101971
  • ElSaid, A. E. R., El Jamiy, F., Higgins, J., Wild, B., & Desell, T. (2018). Optimizing long short-term memory recurrent neural networks using ant colony optimization to predict turbine engine vibration. Applied Soft Computing Journal, 73, 969-991. doi: 10.1016/j.asoc.2018.09.013
  • Fan, W., Wu, X., Shi, X. Y., Zhang, C., Wai Hung, I., Kai Leung, Y., & Zneg, L. S. (2023). Support vector regression model for flight demand forecasting. International Journal of Engineering Business Management, 15(2898). doi: 10.1177/18479790231174318
  • Gudmundsson, S. V., Cattaneo, M., & Redondi, R. (2021). Forecasting temporal world recovery in air transport markets in the presence of large economic shocks: T he case of COVID-19. Journal of Air Transport Management, 91, 102007. doi: 10.1016/j.jairtraman.2020.102007
  • Gui, G., Liu, F., Sun, J., Yang, J., Zhou, Z., & Zhao, D. (2020). Flight delay prediction based on aviation big data and machine learning. IEEE Transactions on Vehicular Technology, 69(1), 140-150. doi: 10.1109/TVT.2019.2954094
  • Guimarães, M., Soares, C., & Ventura, R. (2022). Decision Support Models for Predicting and Explaining Airport Passenger Connectivity From Data. IEEE Transactions on Intelligent Transportation Systems, 23(9), 16005-16015. doi: 10.1109/TITS.2022.3147155
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  • Huang, F., Zhang, T., Wang, Q., & Zhou, D. (2023). CO2 emission change in China’s aviation industry: A fleetwide index decomposition and scenario analysis. Transportation Research Part D: Transport and Environment, 119, 1-16. doi: 10.1016/j.trd.2023.103743
  • IATA. (2016). Forecasts passenger demand to double over 20 years. Retrieved from https://airlines.iata.org/2016/11/23/passenger-numbers-double-2035
  • Iddrisu, A. M., Mensah, S., Boafo, F., Yeluripati, G. R., & Kudjo, P. (2023). A sentiment analysis framework to classify instances of sarcastic sentiments within the aviation sector. International Journal of Information Management Data Insights, 3(2), 100180. doi: 10.1016/j.jjimei.2023.100180
  • İnan, T. T. (2022). Forecasting Recovery Period of the Airfreight Transportation from Covid-19 Pandemic by using Time Series Modelling. Logistics Research, 15(1), 1-18. doi: 10.23773/2022_03
  • Kang, Z., Shang, J., Feng, Y., Zheng, L., Wang, Q., Sun, H., Qiang, B., & Liu, Z. (2021). A deep sequence-tosequence method for accurate long landing prediction based on flight data. IET Intelligent Transport Systems, 15(8), 1028-1042. doi: 10.1049/itr2.12078
  • Lamb, T. L., Winter, S. R., Rice, S., Ruskin, K. J., & Vaughn, A. (2020). Factors that predict passengers willingness to fly during and after the COVID-19 pandemic. Journal of Air Transport Management, 89, 101897. doi: 10.1016/j.jairtraman.2020.101897
  • Lee, J., Marla, L., & Jacquillat, A. (2020). Dynamic Disruption Management in Airline Networks Under Airport Operating Uncertainty. Transportation Science, 54(4), 973-997. doi: 10.1287/trsc.2020.0983
  • Li, Q., Guan, X., & Liu, J. (2023). A CNN-LSTM framework for flight delay prediction. Expert Systems with Applications, 227, 120287. doi: 10.1016/j.eswa.2023.120287
  • Li, X., de Groot, M., & Bäck, T. (2023). Using forecasting to evaluate the impact of COVID-19 on passenger air transport demand. Decision Sciences, 54(4), 394-409. doi: 10.1111/deci.12549
  • Linden, E. (2021). Pandemics and environmental shocks: What aviation managers should learn from COVID-19 for long-term planning. Journal of Air Transport Management, 90, 101944. doi: 10.1016/j.jairtraman.2020.101944
  • Liu, J., Lei, F., Pan, C., Hu, D., & Zuo, H. (2021). Prediction of remaining useful life of multi-stage aero-engine based on clustering and LSTM fusion. Reliability Engineering and System Safety, 214, 107807. doi: 10.1016/j.ress.2021.107807
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  • Oguntona, O. (2020). Longer ‑ term aircraft fleet modelling: narrative review of tools and measures for mitigating carbon emissions from aircraft fleet. CEAS Aeronautical Journal, 11(1), 13-31. doi: 10.1007/s13272-01900424-y
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  • Rahman, T., Zudhy Irawan, M., Noor Tajudin, A., Rizka Fahmi Amrozi, M., & Widyatmoko, I. (2023). Knowledge mapping of cool pavement technologies for urban heat island Mitigation: A Systematic bibliometric analysis. Energy and Buildings, 291. doi: 10.1016/j.enbuild.2023.113133
  • Samli, R., Firat, M., & Yiltas-Kaplan, D. (2021). Forecasting air travel demand for selected destinations using machine learning methods. Journal of Universal Computer Science, 27(6), 564-581. doi: 10.3897/JUCS.68185
  • Selc̣uk, A. M., & Avṣar, Z. M. (2019). Dynamic pricing in airline revenue management. Journal of Mathematical Analysis and Applications, 478(2), 1191-1217. doi: 10.1016/j.jmaa.2019.06.012
  • Suau-Sanchez, P., Voltes-Dorta, A., & Cugueró-Escofet, N. (2020). An early assessment of the impact of COVID-19 on air transport: Just another crisis or the end of aviation as we know it? Journal of Transport Geography, 86, 102749. doi: 10.1016/j.jtrangeo.2020.102749
  • Sulistyowati, R., Kuswanto, H., Series, A. T., & Tsr, R. (2018). Hybrid Forecasting Model To Predict Air Passenger and Cargo in Indonesia. International Conference on Information and Communications Technology, 442447.
  • Sznajder, M., Ratliff, R., & Kaya, C. (2023). A heuristic for incorporating ancillaries into air choice models with personalization (part 2: integrated multinomial logit and hedonic regression models). Journal of Revenue and Pricing Management, 22(2), 140-151. doi: 10.1057/s41272-022-00400-y
  • Tang, Y. (2021). Airline Flight Delay Prediction Using Machine Learning Models. ACM International Conference Proceeding Series, 151-154. doi: 10.1145/3497701.3497725
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  • Tseremoglou, I., Bombelli, A., & Santos, B. F. (2022). A combined forecasting and packing model for air cargo loading: A risk-averse framework. Transportation Research Part E: Logistics and Transportation Review, 158, 102579. doi: 10.1016/j.tre.2021.102579
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  • Wang, T., Zheng, Y., & Xu, H. (2022). A Review of Flight Delay Prediction Methods. International Conference on Big Data Engineering and Education (BDEE), 135141. doi: 10.1109/BDEE55929.2022.00029
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  • Yang, Z., Chen, Y., Hu, J., Song, Y., & Mao, Y. (2023). Departure delay prediction and analysis based on node sequence data of ground support services for transit f lights. Transportation Research Part C: Emerging Technologies, 153, 104217. doi: 10.1016/j.trc.2023.104217
  • Ye, Q., Zhou, R., & Asmi, F. (2023). Evaluating the Impact of the Pandemic Crisis on the Aviation Industry. Transportation Research Record, 2677(3), 1551-1566. doi: 10.1177/03611981221125741
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-edb36139-57a2-4e4f-b543-692bc070b3da
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