Air transport is traditionally measured through flight delays, however mobility is a complex concept. Mobility entails how citizens use the air transport network, including accessibility to airports, individual passenger experiences and airport processes such as check-in security, baggage handling and passenger connectivity.
Measuring air transport mobility using passenger metrics closes the gap between high-level EU policies and concrete goals and metrics for performance for airlines, airports and regions. Measuring mobility also ensures that the focus is on the passenger, helping policy makers to design evidence-based policies that target passenger mobility goals, providing leadership on how to improve mobility in specific geographical areas.
Measuring mobility requires a great deal of historical data. Data are used to calibrate the models and describe stochastically different air transport processes. Mobility assessments require a deep understanding of air transport processes, including air traffic management, airport operations and passenger connectivity. Mercury also leverages on research developing performance metrics and the proficiency of our team in analysing complex datasets to provide performance indicators.
Performance and mobility metrics
Including a range of performance and mobility metrics for decision makers to evaluate different technologies, procedures or regulations.
Fusing a variety of data sources, including European passenger and flight information and incorporating uncertainty in each of the processes modelled.
Capturing passenger value-of-time effects, measuring achievable connections for passengers with multiple legs, based on scheduled times and in-airport gate-to-gate minimum connection times
Capturing airline decision-making regarding passenger re-accommodation, respecting target load factors per route and max seating capacity per aircraft.
Operating under a range of flight and passenger prioritisation scenarios, looking into policy-driven scenarios, airline operationally-driven scenarios (e.g. departure delays and arrival sequences based on delay costs) and ANSP scenarios (e.g. prioritisation of flights based on delay or on passenger numbers).
Capturing airline costs, closely matching the target number pax per flight (stoch. distributed) and including all fleet costs (depretiation, rentals and leases), fuel (and CO2), crew costs (schemes, flight hours, on-costs, overtime), maintenance (extra wear & tear powerplants/airframe), and passenger costs (hard and soft costs, VoT).
Assessing the impacts of various types of disturbance and evaluating novel cost resilience metrics.
Modelling passenger connectivity using credible passenger itineraries, single/multiple flight legs, ticket price and seat class distinction (full itineraries, 2.5 million passengers & 30,000 flights per day)