Detecting and Analyzing Flight Unstable Approaches with QAR Big Data

Abstract

Stable approach is vital for flight safety, and unstable approach is one of the main causes of flight accidents. This study aims to detect flight unstable approaches (FUA) with the quick access recorder (QAR) big data, and analyze the spatio-temporal patterns via exploratory data analysis (EDA) technologies. Results show that the dominant factor of FUA incidents is overrun of airspeed. FUA incidents occurred the most frequently in Shanghai, especially on January 8th and 23th. With combining the meteorological data, we found that the FUA incidents closely relate to weather of spatially varying effects. These findings make practical senses in preventing FUA incidents and safeguarding flights.