Open Aircraft Performance Modeling: Based on an Analysis of Aircraft Surveillance Data

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.