An exploratory data analysis of airport wait times using big data visualisation techniques

Abstract

Airport congestion and wait time influences passenger satisfaction levels in terms of overall travel wait time. The wait time depends on mandatory factors like security, immigration and passenger discretionary factors like airport shopping, dining, and other activities. Passengers rate airport and airline on-time performance through reviews on wait time like the queue for check-in, immigration, security and customs checks, and boarding times. In this paper, we performed an exploratory analysis of airport wait times on customs, border protection data taken from Top 3 busiest airports (Atlanta, Chicago, and Los Angeles) from the United States of America which handle several million passengers every year. We have applied multiple data visualization techniques based on factors like flight arrivals, the number of passengers, booths serving passengers, time of the day, and seasonality patterns. This paper discusses the comparison on these airports with respect to various visualisation. This work can be extended to all airports and can prescribe further analytical techniques to predict wait times based on historical data.