A Data-Driven Framework for Airspace Congestion Analysis Using Aircraft Tracking Data

Project: Research

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Air travel demand increases steadily in the past ten years worldwide, while Asia-Pacificregistered the strongest growth among all regions. Such rapid growth resulted in greaterdegrees of congestions in airspace; subsequent flight delay wastes time, fuel and takes a tollon economic activities and the environment. To improve operational efficiency and avoiddelays, air traffic service providers are actively seeking means to analyze the capacity,efficiency and congestion risks of existing airspace.Existing studies on airspace are insufficient. Agent-based simulation and the QueueingNetwork (QN) model are both the state-of-the-art methods. Both are limited for managingand planning purposes. The former requires extensive data inputs which are practicallychallenging; the latter rests on oversimplified assumptions without consideringcharacteristics of daily operations, i.e. en-route congestions, in-flight holding, deviationsfrom planned route and re-routing, etc. A key limitation for existing methods and theirfurther developments lies in availability of complete operational data. For example, livetracking of aircraft movement, air traffic control commands, fleet scheduling, these datawere heavily regulated by national or regional agencies and airlines without properinformation sharing among them. Air traffic service providers can rarely grasp a ‘bigpicture’ of the regional airspace.New opportunities arise from the implementation of Automatic Dependent Surveillance –Broadcast (ADS-B), a satellite-based surveillance technology which tracks and broadcaststhe location of each aircraft via satellite. With ADS-B adopted by eight countries andgrowing, it is possible for the first time to track and analyze aircraft movement data at globalscale. The ADS-B data allow us to examine in real-time the management of airspace acrossregions and identify bottlenecks that caused air traffic congestion.The aim of this research is to develop a novel framework to analyze airspace congestion in anational air traffic network using real-time aircraft tracking data. The objectives are to (1)characterize airspace operations in a national air traffic network using ADS-B data, (2) builda network queueing model of air traffic network congestions, and (3) demonstrate potentialapplications of the data-driven framework in air traffic management. We will develop atrajectory clustering algorithm to analyze traffic flow patterns; a queueing network modelwill be developed to assess congestions of existing airspace and airports. Results allow airtraffic service providers to identify existing bottlenecks and establish benchmarks for real-timemonitoring. Using the proposed framework, decision-makers can predict risks of flightdelays under various operational scenarios, such as network structure modifications,infrastructure improvements, or changes in air traffic management.The study is an extension of the investigators’ previous and on-going research in airtransportation systems, data analytics and queueing models. A pilot study was conductedusing ADS-B data to compare characteristics of air traffic networks in China and the US.Results suggest that limited airspace capacity in China remains a high risk factor for flightdelays. We will extend the pilot study and develop a data-driven framework that arepotentially useful for air traffic management and planning.This research will enrich literature in air traffic management. The analytical method basedon “big data” analytics of real-time aircraft tracking data is novel. The project will alsocontribute to regional air traffic management modernization and in Hong Kong, toconsolidate the city’s status as a leading air hub in Asia-Pacific and the world.?


Project number9042506
Grant typeGRF
Effective start/end date1/09/17 → …