Analysis of airport ground operations based on ADS-B data

Schultz, Michael; Olivé, Xavier; Rosenow, Judith; Fricke, Hartmut; Alam, Sameer

doi:10.1109/aida-at48540.2020.9049212

Cited by 14 publications

(6 citation statements)

References 21 publications

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“…Likewise, the observation of ‘queuing’ behaviour during which the ground manoeuvre sequence alternates between straight taxi and braking manoeuvres was demonstrated based on the sequencing results from the ADS-B data. Such observations are supported by other recent research work by Schultz et al, which also highlighted that operational impacts whilst aircraft are on the ground can be observed within ADS-B datasets (47) . It is hoped that manoeuvre occurrence and sequencing statistics generated from ADS-B data files could be used to support future design programs along with providing guidance when determining the best practice for constructing landing gear load spectra for fatigue design and analysis (48) .…”

Section: Discussionsupporting

confidence: 84%

Landing gear ground manoeuvre statistics from automatic dependent surveillance-broadcast transponder data

Hoole¹,

Sartor²,

Booker³

et al. 2021

Aeronaut. j.

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Landing gear are exposed to cyclic loads from the ground manoeuvres that aircraft perform in-service. Variability is observed in the loading magnitude associated with ground manoeuvres, along with the per-flight variability in ground manoeuvre occurrence and sequencing. Whilst loading magnitude variability has been widely characterised, significant assumptions are required regarding manoeuvre occurrence and sequencing when constructing landing gear load spectra for fatigue design. These assumptions are required due to the limited availability of data concerning ground manoeuvre occurrence and sequencing relating to aircraft in-service and require validation to facilitate the design of more efficient components. ‘Big-Data’ approaches, employing Automatic Dependent Surveillance-Broadcast (ADS-B) transponder data, enable aircraft ground tracks to be identified. This paper presents a methodology to characterise the variability in ground manoeuvre occurrence and sequencing using ADS-B data sourced from Flightradar24® for a wide-body aircraft fleet. Using statistics generated for the fleet, it was identified that significant variability exists in the occurrence and sequencing of turning and braking manoeuvres. The statistics also validate existing assumptions, including that the proportional share of left and right turning manoeuvres is equal. Finally, this paper discusses the utility of ADS-B datasets for constructing landing gear load spectra and monitoring of landing gear in-service.

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Section: Discussionsupporting

confidence: 84%

Landing gear ground manoeuvre statistics from automatic dependent surveillance-broadcast transponder data

Hoole¹,

Sartor²,

Booker³

et al. 2021

Aeronaut. j.

View full text Add to dashboard Cite

show abstract

“…In terms of assessing collision risk, trajectory estimation and propagation are the basis [31][32][33]. In the U-M encounter, the raw trajectory data of the manned aircraft can be obtained from air traffic control automation systems, such as ADS-B and radars [34][35][36]. For UAVs, there are two surveillance modes, cooperative UAVs and noncooperative UAVs [37] according to the information shared.…”

Section: Literature Reviewmentioning

confidence: 99%

UAV Collision Risk Assessment in Terminal Restricted Area by Heatmap Representation

Zhang

Liu

Low

2023

AIAA SCITECH 2023 Forum

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The frequent intrusion of unmanned aerial vehicles (UAVs) into civil aviation airports poses a severe challenge to the safety management of the airport. The subsequent flight delays and even the closure of the runway have seriously affected the normal operation of the airport. To measure the collision risk of the Unmanned-Manned (U-M) encounter (intruding UAV to manned aircraft), a collision risk assessment platform is developed in this paper to predict the U-M encounter collision risk of the airport restricted area. The designed platform can be used to dynamically generate 3D trajectories of the U-M encounter, and to assess the collision risk with the changing of the relative distance in the probabilistic curve. Further, to predict the collision risk of the airport restricted area, the collision risk heatmap representation is then introduced. A case study of Changi Airport, Singapore is employed to generate the runway collision risk heatmap based on the designed collision risk assessment platform. The results demonstrate how the collision risk of the separate runway and the integrated runway distributes.

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“…This metric is for common points of direct routes and links from or to cluster centers. In the first case, the angle between both lines can be calculated directly as 𝑤(∡(𝑙 𝑖 1 , 𝑙 𝑖 2 )). In the second case, the common point is the cluster center and a route segment moving through this cluster center is not necessarily a straight line but may consist of any sequence of links connected to 𝑐.…”

Section: Structural Complexity Of the Networkmentioning

confidence: 99%

“…The latter results in route changes over time and takes advantage of a dynamically adapting route network. Furthermore, the approach presented here can be applied also to generate a route network for surface traffic of vehicles on airports [1].…”

Section: Introductionmentioning

confidence: 99%

Traffic Network Identification Using Trajectory Intersection Clustering

Gerdes

Temme

2020

Aerospace

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The current airspace route system consists mainly of pre-defined routes with a low number of intersections to facilitate air traffic controllers to oversee the traffic. Our aim is a method to create an artificial and reliable route network based on planned or as-flown trajectories. The application possibilities of the resulting network are manifold, reaching from the assessment of new air traffic management (ATM) strategies or historical data to a basis for simulation systems. Trajectories are defined as sequences of common points at intersections with other trajectories. All common points of a traffic sample are clustered, and, after further optimization, the cluster centers are used as nodes in the new main-flow network. To build almost-realistic flight trajectories based on this network, additional parameters such as speed and altitude are added to the nodes and the possibility to take detours into account to avoid congested areas is introduced. As optimization criteria, the trajectory length and the structural complexity of the main-flow system are used. Based on these criteria, we develop a new cost function for the optimization process. In addition, we show how different traffic situations are covered by the network. To illustrate the capabilities of our approach, traffic is exemplarily divided into separate classes and class-dependent parameters are assigned. Applied to two real traffic scenarios, the approach was able to emulate the underlying route systems with a difference in median trajectory length of 0.2%, resp. 0.5% compared to the original routes.

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Analysis of airport ground operations based on ADS-B data

Cited by 14 publications

References 21 publications

Landing gear ground manoeuvre statistics from automatic dependent surveillance-broadcast transponder data

Landing gear ground manoeuvre statistics from automatic dependent surveillance-broadcast transponder data

UAV Collision Risk Assessment in Terminal Restricted Area by Heatmap Representation

Traffic Network Identification Using Trajectory Intersection Clustering

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