Predictive Facility Utilization Analysis for Airports using IoT and Machine Learning

Airports play a critical role in the international transportation machine, offering everyday services to millions of passengers., management centers are essential to ensure a cozy experience for the passengers and the smooth functioning of airports. By integrating the Internet of Technology (IoT) era and device mastering, the proposed device provides a practical and modern method to improve airport management. The technique offers real-time data on passenger usage of facilities, permitting airport managers to identify developments, beautify layouts, and aid allocation more successfully. To put together destiny demands and enhance the passenger experience, a gadget gaining knowledge of a new prediction version will assist. The version utilizes a Raspberry Pi prepared with an outside digicam and a barcode sensor; it has chosen to seize quite a number of facts streams due to its low charge and versatility. The system can be implemented across critical airport facilities such as restrooms, security checkpoints, and baggage claim areas. The web application visualizes passenger usage of airport facilities using various analytical tools and visualizations. The Support Vector Regression (SVR) algorithm predicts future facility utilization based on historical data. Visualized through pie charts and bar graphs, the results provide actionable insights for airport operators to make informed resource allocation and facility management decisions. Overall, the proposed system has the potential to enhance airport operations significantly, minimize passenger wait times, and boost overall satisfaction, benefiting both airport operators and travelers on a global scale.