Big Data Analytics on Aviation data for the prediction of Airline Trends in Seasonal Delay

Abstract

The trajectory of the global airline industry is pretty much like that of an aircraft. At times it takes off for the high skies and at times, it dips to ground levels. In between these highs and lows, lies the story of the industry of its survival, of the new and emerging trends that fuel its growth. The issue of airline delays, as measured by the number of late arrivals as a percent of total operations, has been of increasing importance in recent years as most of the population chooses air travel as a preferred mode of transportation. This study provides the result about the total flight delay for a specific period of time caused due to climate, security, carrier, NAS, Arrival and Departure based on total number of flights getting delayed over the past few years (2006, 2007 and 2008). The historic data which is to be analysed is stored on the databases such as MongoDB and Hive. The usage of time series analysis along with the integration of heterogeneous database helps to achieve the Airline Seasonal Delay which is implemented and visualized in R. The reports are generated by using time series modeling to provide the insights for the aviation industry to take future measures to avoid delays and manage them.