Interactive Textbook. In the last twenty years, aircraft surveillance has moved from controller-based interrogation to automatic broadcast. The Automatic Dependent Surveillance-Broadcast (ADS-B) is one of the most common methods for aircraft to report their state information like identity, position, and speed. Like other Mode S communications, ADS-B makes use of the 1090 megahertz transponder to transmit data. The protocol for ADS-B is open, and low-cost receivers can easily be used to intercept its signals. Many recent air transportation studies have benefited from this open data source. However, the current literature does not offer a systematic exploration of Mode S and ADS-B data, nor does it explain the decoding process. This book tackles this missing area in the literature. It offers researchers, engineers, and enthusiasts a clear guide to understanding and making use of open ADS-B and Mode S data. The first part of this book presents the knowledge required to get started with decoding these signals. It includes background information on primary radar, secondary radar, Mode A/C, Mode S, and ADS-B, as well as the hardware and software setups necessary to gather radio signals. After that, the 17 core chapters of the book investigate the details of all types of ADS-B signals and commonly used Mode S signals. Throughout these chapters, examples and sample Python code are used extensively to explain and demonstrate the decoding process. Finally, the last chapter of the book offers a summary and a brief overview of research topics that go beyond the decoding of these signals. @en

The usage of large private and business jets, from those owned by Elon Musk to Kylie Jenner and Bernard Arnault, has recently attracted considerable attention in many countries. Enabled by open and crowdsourced aircraft tracking systems based on the automatic dependent surveillance–broadcast protocol, the aircraft and their owners have been scrutinized. While the underlying technology is not novel and its privacy issues have been discussed for years, the increased attention has led to the backlash against open tracking data and, consequently, a scramble to find possible solutions to hide private jets from the public eye. In this paper, we analyze two such methods, which have not yet been discussed previously in the literature: blocking requests to web tracking platforms and malicious editing of crowdsourced databases. We draw on data from the OpenSky Network and illustrate the futility of such approaches. Finally, we outline the type of stakeholders and aircraft deploying such methods, as well as demonstrate the level of environmental impact that might have otherwise been missed by the public.@en

As global flight volume rises, the aviation industry is facing increasing climate challenges. One major factor is the impact of contrails, which trap outgoing terrestrial radiation and counteract emission reduction benefits from emission-optimized flight routes. Our study quantifies contrail-forming flights globally and assesses altitude adjustments necessary to avoid these regions. Using the Integrated Global Radiosonde Archive and global flight data from 2021-2022, we highlight several contrail-prone regions with high air traffic volumes and high potential for contrail-formation. We propose an operational strategy in altitude diversion, which can halve the amount of persistent contrails. Further, we analyse the additional carbon emissions caused by the altitude diversions and safety risks in terms of potential new conflicts. Our findings provide actionable strategies for policymakers to balance climate mitigation and operational challenges in aviation.@en

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.@en

Air traffic simulations serve as common practice to evaluate different concepts and methods for air transportation studies. The aircraft performance model is a key element that supports these simulation-based studies. It is also an important component for simulation-independent studies, such as air traffic optimization and prediction studies. Commonly, contemporary studies have to rely on proprietary aircraft performance models that restrict the redistribution of the data and code. To promote openness and research comparability, an alternative open performance model would be beneficial for the air transportation research community. In this paper, we introduce an open aircraft performance model (OpenAP). It is an open-source model that is based on a number of our previous studies, which were focused on different components of the aircraft performance. The unique characteristic of OpenAP is that it was built upon open aircraft surveillance data and open literature models. The model is composed of four main components, including aircraft and engine properties, kinematic performances, dynamic performances, and utility libraries. Alongside the performance model, we are publishing an open-source toolkit to facilitate the use of this model. The main objective of this paper is to describe each main component, their connections, and how they can be used for simulation and research in practice. Finally, we analyzed the performance of OpenAP by comparing it with an existing performance model and sample flight data.@en

A large number of stakeholders exist in the modern air traffic management ecosystem. Air transportation studies benefit from collaboration and the sharing of knowledge and findings between these different players. However, not all parties have equal access to information. Due to the lack of open-source tools and models, it is not always possible to undertake comparative studies and to repeat experiments. The barriers to accessing proprietary tools and models create major limitations in the field of air traffic management research. This dissertation investigates the methods necessary to construct an aircraft performance model based on open data, which can be used freely and redistributed without restrictions. The primary data source presented in this dissertation is aircraft surveillance data that can be intercepted openly with little to no restriction in most regions of the world. The eleven chapters in this dissertation follow the sequence of open data, open models, and performance estimations. This order corresponds to the three main parts of the dissertation. In the first part of the dissertation, open surveillance data is explored. Methods are developed to decode and process this data. Extraction of information is also made possible thanks to machine learning algorithms. The second part of the dissertation examines the main components of the open aircraft performance model. Models related to kinematics, thrust, drag polar, fuel flow, and weather are investigated. The third part of the dissertation looks into the possibility of using surveillance data to estimate aircraft performance parameters, for example, aircraft turn performance, aircraft mass, and thrust settings, for individual flights. With the goal of making future air traffic management studies more transparent, comparable, and reproducible, the models and tools proposed in this dissertation are fully open. The final aircraft performance model, OpenAP, proposed in this dissertation has proven to be an efficient open alternative to current closed-source models.@en

The vertical trajectory plan (altitude and speed) corresponding to the descent phase of a modern airliner is computed by the on-board flight management system while the aircraft is still in cruise. As long as the constraints on the arrival procedure allow, this system plans for an idle descent and the exact location of the (optimal) top of descent (TOD) is determined in this process. In busy terminal airspace, however, air traffic control officers – motivated by the needs to maintain a safe and expeditious flow of aircraft – might require to start the descents before the TOD computed by each particular arriving aircraft. In such situations, most flight guidance systems aim to intercept the original altitude plan from below, by using a shallower descent angle while keeping the speed plan, requiring in this way, additional thrust. This leads, consequently, to higher fuel consumption figures. The objective of this paper is threefold. Firstly, it characterises and quantifies these fuel inefficiencies for an Airbus A320, using accurate aircraft performance data and a trajectory computation software from the manufacturer. Secondly, it proposes a methodology to automatically identify early descents and to extract the key parameters required to compute the fuel inefficiencies by only observing ADS-B (automatic dependent surveillance-broadcast) data. Finally, the method is applied to a case study with 4,139 real ADS-B trajectories in Amsterdam-Schiphol (The Netherlands) terminal airspace; showing that early descents are very frequent and that they increase the fuel consumption by a 5%, in average. @en

The environmental impact of aviation has become the focus of increased concerns for policymakers around the world. The recent pandemic provided many interesting case studies on the impact of aviation on the environment. Following the initial COVID-19 containment measures and hard lockdowns, the sharp decrease in aircraft movements caused a measurably improved air quality worthy of further study.The OpenSky Network has acted as an important open data source for aviation research since 2013. In this paper, we analyze one year of fine-grained pre-COVID air traffic trajectories (comprising the entire year 2018) to estimate fuel consumption and pollutant emissions in the aviation industry. We compare this large-scale big data processing approach to a reduced model approach based solely on global commercial aircraft movement schedules collected from airlines and airports, aggregated by a commercial provider.Our study quantifies the impact of commercial aviation on global emissions. The numbers reveal that aviation's CO2 emissions contribute to 2% of global emissions and that commercial aviation contribution remains a proxy for countries' wealth.@en

Established in 2013, The OpenSky Network, a crowdsourced network of ADS-B receivers, has consistently collected surveillance data from equipped aircraft and made it available for science. Coverage has steadily improved, boasting more than 6000 registered sensors worldwide today. This platform has aided numerous researchers in publishing studies across fields such as air traffic management, security, environment, and radio frequency interference. Following the 2020 mandate, most aircraft flying at high altitudes in Europe or Northern America are within range of one of the network’s ADS-B receivers. To complement existing research using OpenSky data, this paper focuses on lower altitude coverage, including light aircraft, general aviation, gliders, and ultralights, which are not required to carry ADS-B transponders. Instead, these often use, esp. in Europe, another traffic awareness and collision avoidance technology known as FLARM. The OpenSky Network has been gathering FLARM messages since 2018, and now enough data is available for a detailed analysis. The aim of this report is to present OpenSky’s FLARM data, explain the workings of the technology, and highlight potential uses of this data for future research.@en