Assessing airport surface traffic performance from open sources of aviation data

This study investigates the potential of crowdsourced open aviation data, specifically ADS-B trajectories and Open-StreetMap infrastructure models, to analyse airport surface operations. Surface position messages in ADS-B often suffer from challenges related to Global Navigation Satellite System localization accuracy and Mode S receiver data reception, such as multipath interference. Similarly, the crowdsourced efforts to enrich OpenStreetMap’s infrastructure data are primarily focused on general mapping purposes rather than detailed operational analysis. In this paper, we review the existing literature on airport surface performance assessment and develop a methodology to adapt these techniques for open ADS-B data. Our approach focuses on filtering trajectory data to map positional information, where available, into a timed sequence of taxiway segments. These sequences are used to calculate key performance indicators from the literature, such as saturation rates, taxi-out time distributions as a function of departing aircraft queue sizes, and flow-density relationships (fundamental diagrams). We apply this methodology to a data set of selected days in 2024 at Zurich International Airport.