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Introduction

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The dynamics of flight price predictions

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Flight pricing models have long been of interest to researchers, +airline companies, and consumers alike (Sun et al. 2024). As global air +travel expands, so do the complexities involved in predicting flight +prices due to the dynamic nature of factors such as fuel costs, demand +fluctuations, and the regulatory environment (Sun +et al. 2024; Belobaba, Odoni, and Barnhart 2015; Association et al. +2019; Borenstein and Rose 1994). Predicting flight prices +accurately allows both passengers and airlines to optimize travel +schedules, with potential economic and environmental benefits (Belobaba, Odoni, and +Barnhart 2015). In recent years, machine learning techniques have +been widely adopted in various industries, including aviation, to model +complex relationships and predict outcomes such as pricing (Sherly +Puspha Annabel et al. 2023; Kalampokas et al. 2023; Xu et al. +2021). For this project, we propose a machine learning-based +predictive model designed to estimate future flight prices based on +historical data from domestic flight routes to and from India.

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The importance of predicting flight prices

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Accurately predicting flight prices has significant implications for +multiple stakeholders. For consumers, anticipating price trends helps +optimize travel costs, allowing for better budgeting and planning (Association +et al. 2019; Borenstein and Rose 1994; Gillen and Morrison 2005). +Airlines, on the other hand, can improve their revenue management +strategies through dynamic pricing, ensuring that flights operate closer +to capacity while adjusting pricing to meet seasonal demand shifts and +market competition (Belobaba, +Odoni, and Barnhart 2015; Borenstein and Rose 1994; Gillen and Morrison +2005). Additionally, by integrating environmental factors such as +carbon emissions into the pricing model, this research can help airlines +design more sustainable routes, potentially leading to fewer emissions +through better traffic management and efficient flight operations (Sun +et al. 2024; Association et al. 2019; Gillen and Morrison 2005; Xiong et +al. 2023). The aviation industry contributes significantly to the +global economy but also poses challenges due to its substantial carbon +footprint (Brueckner and Abreu 2017).

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Methodological approach and dataset

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This project utilizes a Kaggle dataset on domestic flights in India, +covering variables such as airline name, total stops, departure and +destination locations, and flight prices [3–5, 8]. Using regression +techniques within a machine learning framework, the researchers aim to +identify which variables most significantly impact flight prices. +Initial findings suggest that factors such as the number of stops, +flight duration, and seasonal demand (correlating with major holidays in +India) are key drivers of price variation. For instance, a notable spike +in flight prices is observed around holidays in India, are insights that +not only advance knowledge in transportation economics but also serve as +a crucial decision-making tool for airline pricing strategies (Sun +et al. 2024; Association et al. 2019; Borenstein and Rose 1994; Gillen +and Morrison 2005).

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Multidisciplinary implications

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By developing this predictive model, the research bridges the gap +between economics, machine learning, and environmental sustainability. +Future applications of this work could extend beyond pricing into areas +like route optimization and emission reduction strategies (Association et +al. 2019). As flight prices are a key variable in both economic +and environmental calculations, understanding their underlying dynamics +can contribute to more sustainable and cost-effective air travel in the +future (Belobaba, +Odoni, and Barnhart 2015; Association et al. 2019; Gillen and Morrison +2005).

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+Association, International Air Transport et al. 2019. “Economic +Performance of the Airline Industry.” URL: Https://Www. Iata. +Org/Whatwedo/Documents/Economics/IATA-Economic-Performance-of-the-Industry-End-Year-2014-Report. +Pdf (Дата Обращения: 22.12. 2015). +
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+Belobaba, Peter, Amedeo Odoni, and Cynthia Barnhart. 2015. The +Global Airline Industry. John Wiley & Sons. +
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+Borenstein, Severin, and Nancy L Rose. 1994. “Competition and +Price Dispersion in the US Airline Industry.” Journal of +Political Economy 102 (4): 653–83. +
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+Brueckner, Jan K, and Chrystyane Abreu. 2017. “Airline Fuel Usage +and Carbon Emissions: Determining Factors.” Journal of Air +Transport Management 62: 10–17. +
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+Gillen, David, and William G Morrison. 2005. “The Economics of +Franchise Contracts and Airport Policy.” Journal of Air +Transport Management 11 (1): 43–48. +
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+Kalampokas, Theofanis, Konstantinos Tziridis, Nikolaos Kalampokas, +Alexandros Nikolaou, Eleni Vrochidou, and George A Papakostas. 2023. +“A Holistic Approach on Airfare Price Prediction Using Machine +Learning Techniques.” IEEE Access 11: 46627–43. +
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+Sherly Puspha Annabel, L, G Ramanan, R Prakash, and S Sreenidhi. 2023. +“Machine Learning-Based Approach for Airfare Forecasting.” +In Proceedings of International Conference on Data Science and +Applications: ICDSA 2022, Volume 2, 901–12. Springer. +
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+Sun, Xiaoqian, Changhong Zheng, Sebastian Wandelt, and Anming Zhang. +2024. “Airline Competition: A Comprehensive Review of Recent +Research.” Journal of the Air Transport Research +Society, 100013. +
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+Xiong, Xueli, Xiaomeng Song, Anna Kaygorodova, Xichun Ding, Lijia Guo, +and Jiashun Huang. 2023. “Aviation and Carbon Emissions: Evidence +from Airport Operations.” Journal of Air Transport +Management 109: 102383. +
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+Xu, Xu, Clare Anne McGrory, You-Gan Wang, and Jinran Wu. 2021. +“Influential Factors on Chinese Airlines’ Profitability and +Forecasting Methods.” Journal of Air Transport +Management 91: 101969. +
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