Year: 2025
Type: article
Abstract: Flight delays are one of the most significant issues in the aviation sector, resulting in money-related calamities for planes and inconvenience for passengers. with millions of flights operating annually, increasing discuss activity has led to congestion and wasteful operational aspects. delays are influenced by various variables, including climate conditions, discuss activity, specialized issues, and airplane terminal operations. in order to solve this problem, we introduce a deep learning-based demonstrate for flight delay expectation based on verifiable and real-time data. our strategy involves examining crucial variables like flight plans, climate designs, and flying machine operations. We will implement deep learning calculations, including LSTM and RNN, to separate designs and predict delays accurately. The think about refers to the comparison of the performance of different models to determine the most effective approach. the results of this research can assist carriers in making informed decisions, reducing delays, lowering costs, and enhancing traveler satisfaction through much improved operation management and planning.
Authors: Geetha Sri Yendrapati, Sonti Sai Trishal, Venigalla Bala Karthik, Tehmina Ishak, Tamma Lithish babu, Satvik Dasari
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4408873371