This article is published in the Case Study category.
In the “Airport Congestion Analysis with Big Data Analysis” study, researchers analyzed the passenger traffic data to predict congestion in the airport building. Analysis was carried out by identifying the traffic routes of passengers in the airport building by day of the week and time by using Wi-Fi sensor collectors. The traffic routes analysis result showed that noon to 2 pm was the most congested period for all facilities, which could be managed to improve major facilities. The result of regression analysis showed that the self-check-in reduces congestion and check-in counters increase congestion. These findings provide important implications for operations, including congestion management at airports. In turns, the airports can optimize their managements and improve passenger experience.
The full article is available at Korea Science
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