Dynamic Flight Price Prediction Using Machine Learning Algorithms

Gupta, Chitranjan Kumar; Kushwaha, Om Nath; Yadav, Amit; Kumar, Vinod

doi:10.1109/icac3n56670.2022.10074448

Cited by 2 publications

(1 citation statement)

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“…The aviation industry is known for its dynamic and complicated nature, where several elements such as demand, fuel costs, and route difficulties continually impact the structure of airfare pricing [1,2]. The accurate prediction of flight price is crucial for airlines, travel agents, and consumers alike, as it facilitates informed decision-making, effective resource allocation, and enhanced travel planning.…”

Section: Introductionmentioning

confidence: 99%

Flight Price Prediction Web-based Platform: Leveraging Generative AI for Real-time Airfare Forecasting

Guan

2024

JWE

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The aviation business encounters difficulties in correctly and swiftly predicting flight fares due to the dynamic nature of the sector. Factors such as variations in demand, fuel costs, and the intricacies of various routes have an impact on this. This work presents a new method to tackle this issue by utilizing generative artificial intelligence (GAI) approaches to accurately forecast airfares in real-time. This paper presents a novel framework that integrates generative models, deep learning architectures, and historical pricing data to improve the precision of future flight price predictions. The study employs a GAI within a cutting-edge web engineering framework. This approach is designed primarily to gather knowledge about complex patterns and relationships present in historical airline data. Through the utilization of this methodology, the model is able to accurately perceive complex connections and adjust to ever-changing market conditions. Our model utilizes deep neural networks to effectively handle various circumstances and extract vital information, so facilitating a comprehensive comprehension of the intricate elements that impact flight cost. Moreover, the suggested approach places significant emphasis on precisely predicting upcoming occurrences in real-time, facilitating prompt reactions to market volatility and offering a valuable resource for airlines, travel agents, and customers alike. In order to enhance the accuracy of real-time forecasts, we utilize a web-based platform that allows for smooth interaction with live data streams and guarantees swift updates. The results demonstrate the model’s capacity to adjust to dynamic market conditions, rendering it an attractive option for stakeholders in search of precise and current forecasts of flight prices.

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Section: Introductionmentioning

confidence: 99%