Cluster-Based Flight Data Analysis and Visualization Software

Description

Airlines have begun an extensive monitoring of flight operations, accumulating an abundance of flight data. However, current software tools to analyze flight data have limited capabilities, leaving valuable information in the data untapped. This project aims to offer airlines an advanced analytical tool to unlock the hidden value in flight data for safety improvement. We plan to develop a software tool for flight data analysis and visualization using data mining methods. The software will enable airline decision makers to see analytics presented visually, grasp latent patterns, and obtain valuable insights about their flight operations. To develop this software, we will 1) develop a cluster-based flight data analysis algorithm to reveal the hidden patterns in flight data, 2) design effective visualizations for decision makers to understand analytical results from the algorithm, and 3) develop a prototype of interface for airline users. This software could be used to improve airline safety management and pilot training. A successful development of this technology could enhance Hong Kong’s stature as a leading international hub for air transportation. Also, the technology could be adapted to other modes of transport, enable various users to be better informed and make safer and 'smarter' use of transportation systems.