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Aero Predict & Flight Booking
Subject area: Science,Engineering and Technology · Area of research: Computer Engineering, IT, Data Science
Abstract
Given the exponential rise and increasing complexity of air travel in recent decades, there is a pressing demand for intelligent, data-driven solutions that can optimize airline operations and enhance the customer experience. This study presents Aero Predict & airline Booking, an integrated web-based platform that blends machine learning-powered predictive analytics with an interactive, user-friendly airline booking experience in order to address this difficulty. The system's primary objectives are: 1. To forecast critical flight parameters, such as delays, weather-related interruptions, and changes in ticket costs, using machine learning models trained on large real-time and historical aviation datasets. 2. To streamline flight search and selection by leveraging user preferences, booking history, and current travel trends in order to deliver a seamless and personalized booking experience. The predictive analytics feature helps passengers make informed booking decisions by forecasting the probability of delays and interruptions based on factors such as airline schedules, departure and arrival airports, travel dates, and previous data. Furthermore, the booking interface suggests flights intelligently, which enhances the usefulness and relevance of search results. System performance was evaluated by comparison studies with conventional booking platforms. The results revealed significant improvements in forecast accuracy, user engagement, and booking satisfaction. In order to keep users updated during their trip, the system also offers automated notifications and real-time flight updates. By combining artificial intelligence with aviation services, this project provides a scalable and adaptable system that can be extended to more extensive transportation and logistics applications. It lays the groundwork for future advancements in predictive travel technologies, including enhanced mobile functionality, more thorough interaction with airline databases, and advanced user behaviour analytics.
Keywords
Flight Delay Prediction, Machine Learning, Predictive Analytics, Smart Travel Solutions, Personalized Booking, Aviation Data, Web-based Platform.
References
[1] Zhang, Y., & Xie, Y. (2019). User-centered intelligent travel planning using real-time data and recommendations. Journal of Advanced Transportation, 2019, Article ID 3456148. https://doi.org/10.1155/2019/3456148
[2] Kharabsheh, R., Alshurideh, M., Al Kurdi, B., & Salloum, S. (2020). Data analytics and decision-making in aviation: A comprehensive review. International Journal of Information Technology and Decision Making, 19(3), 1045–1065. https://doi.org/10.1142/S0219622020500132
[3] Hopper Inc. (n.d.). Hopper travel app—Flight price predictions and booking. Retrieved from https://www.hopper.com/
[4] Google Flights. (n.d.). Flight price insights and predictions. Retrieved from https://www.google.com/flights
[5] Amadeus IT Group. (2020). Big data and AI in aviation: A technical overview. Retrieved from https://amadeus.com/
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How to cite this paper
@article{1708922,
author = {Pratik S. Hake, Pratik P. Parjane, Suraj S. Parte, Liyakat S. Subhedar, Prof. Nita Dimble},
title = {Aero Predict & Flight Booking},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {12},
pages = {106-111},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1708922.pdf},
abstract = {Given the exponential rise and increasing complexity of air travel in recent decades, there is a pressing demand for intelligent, data-driven solutions that can optimize airline operations and enhance the customer experience. This study presents Aero Predict & airline Booking, an integrated web-based platform that blends machine learning-powered predictive analytics with an interactive, user-friendly airline booking experience in order to address this difficulty. The system's primary objectives are: 1. To forecast critical flight parameters, such as delays, weather-related interruptions, and changes in ticket costs, using machine learning models trained on large real-time and historical aviation datasets. 2. To streamline flight search and selection by leveraging user preferences, booking history, and current travel trends in order to deliver a seamless and personalized booking experience. The predictive analytics feature helps passengers make informed booking decisions by forecasting the probability of delays and interruptions based on factors such as airline schedules, departure and arrival airports, travel dates, and previous data. Furthermore, the booking interface suggests flights intelligently, which enhances the usefulness and relevance of search results. System performance was evaluated by comparison studies with conventional booking platforms. The results revealed significant improvements in forecast accuracy, user engagement, and booking satisfaction. In order to keep users updated during their trip, the system also offers automated notifications and real-time flight updates. By combining artificial intelligence with aviation services, this project provides a scalable and adaptable system that can be extended to more extensive transportation and logistics applications. It lays the groundwork for future advancements in predictive travel technologies, including enhanced mobile functionality, more thorough interaction with airline databases, and advanced user behaviour analytics.},
keywords = {Flight Delay Prediction, Machine Learning, Predictive Analytics, Smart Travel Solutions, Personalized Booking, Aviation Data, Web-based Platform.},
month = {June},
}