Andrew Cook of the University of Westminster is the coordinator of the Passenger-Oriented Enhanced Metrics (POEM), which received the “Outstanding Project” award from the SESAR Joint Undertaking at a ceremony in Madrid on 5 March. In this article, he explains the project’s impetus and outcomes, and why it is deserving of the recent accolade..

Why are passenger-centric metrics important for air transport and air traffic management performance?

Although passengers are the ultimate customers of the air transport system, we may sometimes lose sight of this. Social and political priorities in Europe are continuing to shift in further support of passenger mobility, as evidenced by high-level position documents such as ‘Flightpath 2050’ and the European Commission’s ‘Roadmap to a Single European Transport Area’. This is being backed up by regulatory review. To better measure progress in reaching such objectives, passenger-centric metrics are needed. These are largely absent from the metrics currently in place to measure air transport system performance. It is certainly not always the case that passenger-centric and flight-centric metrics behave in the same way. Sometimes they even move in opposite directions. Our work set out to explore these effects further and to quantify such issues.

Describe the work of the Passenger-Oriented Enhanced Metrics (POEM) project and why it is innovative?

We built the first full European network simulation model with explicit passenger itineraries and full airline delay cost estimations. At the core of the project was the design of new air transport performance metrics and their evaluation under novel flight and passenger prioritisation scenarios. We modelled the busiest 199 ECAC airports, including routes between them and 50 major airports outside Europe. The assignment of 2.5 million passengers to individual flights on our simulation day, based on IATA itinerary data, plus the inclusion of full airline cost of delay models, were fundamental components of POEM. Using detailed and realistic airline decision-making rules, we were able to model passengers’ missed connections due to delays and cancellations during nominal and disturbed operations. These impacts are often missing from performance models, or only estimated statistically. We also used complexity science techniques to better characterise the propagation of delay through the network.

What has the project revealed regarding the usefulness of passenger metrics?

The project allowed the exploration of performance using both passenger-centric and (new and existing) flight-centric metrics. We found that simple flight prioritisation rules, e.g. based on passenger numbers only, were ineffective, whilst passenger prioritisation rules at airports only made a positive impact when current airline rebooking constraints were relaxed. Some of the prioritisation strategies explored, for example based on airline cost minimisation rules, resulted in win-win outcomes for airlines and passenger alike. A key finding throughout was that simple flight-centric metrics (such as flight delays) are often not sufficient. If you want to see the full impacts of operational change, then passenger-centric metrics are needed. Furthermore, the complexity science techniques applied allowed us to better understand reactionary (knock-on) delays. These account for almost half of all delays in Europe. To give an example, under some prioritisation scenarios, an important trade-off was revealed: the propagation of delay was contained within smaller airport communities, but these communities themselves became more susceptible to such propagation.

How do you believe the results of POEM can serve future ATM research and development, and more broadly air transport?

Firstly, we hope to have demonstrated the need to include passenger-centric performance in the wider context of the performance-based paradigm in ATM, shedding some additional light on the trade-offs and complex interdependencies at work. It is commonly recognised that there is plenty of scope for further work here, although data availability remains a barrier. Secondly, the model itself is fully flexible, such that we hope to build further on the results. Focusing on ATM, we are able to explore future prioritisation rules (with clear links to the User Driven Prioritisation Process) and network resilience under perturbation (from localised airport disruption to ash clouds – the latter by integrating other transport modes). The model can also be integrated with existing airline tools for disruption management. Looking more broadly towards air transport, we can explore the impacts of changes in airline practice (both airborne and at airports), evolving market trends (such as aircraft sizes and alliance structures) and, of course, future EU policy.


POEM project partners: