WRAP: An open-source kinematic aircraft performance model

Abstract

Open access to flight data from Automatic Dependent Surveillance-Broadcast (ADS-B) has provided researchers with more insights for air traffic management than aircraft tracking alone. With large quantities of trajectory data collected from a wide range of different aircraft types, it is possible to extract accurate aircraft performance parameters. In this paper, a set of more than thirty parameters from seven distinct flight phases are extracted for common commercial aircraft types. It uses various data mining methods, as well as a maximum likelihood estimation approach to generate parametric models for these performance parameters. All parametric models combined can be used to describe a complete flight that includes takeoff, initial climb, climb, cruise, descent, final approach, and landing. Both analytical results and summaries are shown. When available, optimal parameters from these models are also compared with the Base of Aircraft Data and the Eurocontrol aircraft performance database. This research presents a comprehensive set of methods for extracting different aircraft performance parameters. It also provides the first set of open parametric performance data for common aircraft types. All model data are published as open data under a flexible open-source license.

Trajectory Prediction Sensitivity Analysis Using Monte Carlo Simulations Based on Inputs’ Distributions

“…All estimated distribution functions (in both graphical and tabular form) as well as their equations can be accessed as a data collection [27]. Furthermore, the average values of CAS and Mach number distributions estimated in this study are comparable to those found in the research on constructing an open-source aircraft performance model utilizing ADS-B data [28]. The average values of the speed distributions were also compared with the BADA [29] values and were found to be within 20 knots for CAS and within 0.02 for Mach number.…”

Modeling Land and Hold Short Operations: Balancing Safety and Arrival Rate

“…Finally, the researchers made several assumptions regarding the distributions in the stochastic model. When inspecting the real-world aircraft landing speed data in the ADS-B database, all followed a normal distribution (Sun et al, 2019). Normal distributions were therefore assumed to impute the remainder of the aircraft landing speeds.…”

“…The model was developed by using multiple sources, prioritizing those with more data fidelity. First, 10 of the 23 fleet types servicing CLT daily had data available in an open-source aircraft performance model, based upon data mining of real-world ADS-B (flight data broadcast) data (Sun et al, 2019). The data included landing speed and braking rate mean, minimums, maximums, and type of data distribution.…”

“…The remaining values of the standard deviation, minimum, and maximum were imputed. All landing speeds were assumed to be normally distributed, as all observed ADS-B landing speed distributions were normal (Sun et al, 2019). The known standard deviation of the Embraer 190 airspeed was 14.0 knots, while the known mean standard deviation of all larger aircraft was 8.1.…”

Forecasts of future scenarios for airport noise based on collection and processing of web data

“…In recent times, however, the introduction of ADS-B transponders has given rise to flight tracking websites such as Flightradar24 [9] and FlightAware [10], which use and rearrange information from these transponders and other sources to provide the public with flight movement data in real time. The available amount of information, steadily growing thanks to EU's obligation to install ADS-B on all large aircraft by 2020 [11], is already large enough to enable statistical analysis of aircraft performance [12]. Moreover, when these data are paired with additional Internet-based sources such as aircraft model databases, it becomes possible to define flight events at an airport and use them as an input to a best-practice model.…”