Real‐Time Flight Delay Prediction with Live Data from IoT and Airline Operations Optimization Using the KNN Algorithm

The swift growth of air travel highlights the necessity of effective airline management and precise flight delay predictions. The objective of this project is to create a real-time flight delay prediction system and investigate how airline operations may be optimized by using it. Combining established approaches with machine learning techniques enables the aviation industry to make better decisions by offering insightful information about anticipated delays. The first step is creating a large dataset that includes all the factors that affect flight delays, including weather, past performance, airport traffic, and aircraft status. Using this information, the research applies machine learning models to dynamically determine the likelihood of aircraft delays, concentrating on k-nearest neighbors (KNN). Although KNN is used as an example, it is important to remember that more advanced models, such as random forests, support vector machines, or deep learning networks, are more appropriate for precisely forecasting actual flight delays. Beyond simple forecasting, the research explores how accurate projections might be integrated into improving airline operations. Airlines can enhance operational efficiency by optimally allocating resources, managing crew rosters, adjusting gate assignments, and monitoring projected delays in conjunction with current schedules. In this optimization process, algorithms are essential for reducing disruptions and improving passenger experience. The project's results highlight the potential advantages of flight delay prediction and real-time operational optimization in reducing the negative effects of delays on both airlines and passengers. They emphasize how crucial it is to gather, prepare, and analyze data meticulously to reap these benefits. The project's results highlight the potential advantages of flight delay prediction and real-time operational optimization in reducing the negative effects of delays on both airlines and passengers, emphasizing the crucial role of Internet of Things in data gathering for these benefits.