An Interactive Airport Management System using Predictive Analysis

Airport Traffic Management is a very important and necessary operation for any Airport authorities maintaining the airports across the Globe. There is proper analysis model require to predict the amount of passengers planning to visit the airport during the specific time period. The After this analysis, based on the predictions, the airports authorities can do queue management at the boarding pass counters and then an Android app can be used for emergency boarding pass generation of passengers which will allow them to board flights successfully. This will avoid the last time rushes and long waiting time on the queue for the passenger want fly through particular airport. Many survey papers have been published regarding the Airline Traffic Management but there is no sufficient evidence that proves the required amount of arrangements and alternate options to maintain the airport crowd in a fair way. The Proposed work focus on performing Big Data Analytics over Airline traffic datasets collected over the period of time. The given datasets are classified using Modified Timeseries and Decision making algorithm. From the data analysis results the suggestions can be given to specific airports about the number of people coming to the particular airport at a particular dateThe entire model is implemented using python and the results are outperforming the existing results. From the performance analysis it shows that the current model provides the better prediction this will help the Airport Authorities to improve their service.