Analyzing and Detecting Runway Incursions Using High-Resolution ADS-B and Incident Databases

This study focuses on enhancing aviation safety by detecting and analyzing runway incursionsunauthorized entries of aircraft, vehicles, or personnel onto active runwaysusing high-resolution ADS-B data from Flightradar24 (FR24). Despite existing protocols, such incursions remain a significant threat to air traffic operations. The project aims to develop and validate algorithms capable of identifying critical events, such as simultaneous runway occupancy and near-misses, by leveraging FR24s historical ADS-B data. These data, sampled every six seconds during ground phases and every thirty seconds during cruise, will be cross-referenced with known incidents from IATAs ACC_INC database and additional datasets from the FAA and IATA. The study will also examine contributing factors like airport geometry, weather, and runway conditions. Each incursion will be categorized by severityfrom high-risk (Category A) to low-impact (Category D)to identify patterns and risks over the past two years. In partnership with IATA, the project seeks to enhance early risk detection and situational awareness using scalable, data-driven tools. The outcomes will inform global safety standards, regulatory initiatives, and best practices. A final report, scheduled for 2026, will provide actionable recommendations aimed at reducing the frequency and severity of runway incursions worldwide.

Faculty Supervisor:

Georges Ghazi;Julio Montecinos

Student:

Partner:

International Air Transport Association

Discipline:

Engineering

Sector:

Other services (except public administration); Professional, scientific and technical services; Transportation and warehousing

University:

École de technologie supérieure

Program: