ATM application-oriented Research for Multimodality and Passenger Experience

Project results are expected to contribute to the following expected outcomes.

• Environment. Project results are expected to demonstrate the positive impact that the proposed solutions would have on the environment (i.e. in terms of emissions, noise and/or local air quality) by alleviating congestion at and around airports through better prediction of passenger flows (e.g. using multimodal decision-making tools, single ticketing, etc.), by supporting access to / egress from airports by environmentally friendly means, etc.
• Passenger experience. Project results are expected to improve the passenger experience by sharing data on air transport with travel service providers, thus helping passengers plan intermodal journeys that include air segments. This will also be achieved through appropriate analysis of traveller behaviour, modelling and simulation.
• Capacity. Project results are expected to contribute to capacity by providing methods for journey optimisation and personalisation of offers to passengers, especially in the event of disruption (e.g. enabling passengers to rebook and replan travel using the same or different means of transport).
• Cost-efficiency. Project results are expected to demonstrate that new data-sharing standards, together with a tailored multimodal performance scheme and governance, will allow new ‘as a service’ businesses, adding value for aviation within an integrated transport system.
• Safety. Project results are expected to maintain at least the same level of safety as the current ATM system.

The SESAR 3 JU has identified the following innovative research elements that could be used to achieve the expected outcomes. The list is not intended to be prescriptive; proposals for work on areas other than those listed below are welcome, provided they include adequate background and justification to ensure clear traceability with the R&I needs set out in the SRIA for the multimodality and passenger experience flagship.

• Future airport business model. This research is about how the airport business model of the future is expected to evolve in terms of adjustments to emerging/changing passenger requirements, airline business models and integrating new procedural requirements (R&I need: access to / exit from the airport: airports are obvious multimodal nodes for aviation).
• Multimodal governance. This element covers the governance and standards to facilitate coordination between modes of transport in a multimodal environment; the need for regulation to ensure a level playing field for service providers, preventing market dominance or uncompetitive pricing from limited providers and not limiting market access for others; and multimodal trip pack creation and corresponding insurance. In addition, there is a need to investigate ensuring that security policies for air and other modes of transport (e.g. rail) are complementary, especially considering the importance of reducing administrative burdens (R&I need: access to / exit from the airport: airports are obvious multimodal nodes for aviation).
• Multimodal decision-making tools. This research covers the development of decision support systems for intermodal solutions to manage systems at tactical and/or strategic level (e.g. collaborative optimisation of passenger or goods flows across a multimodal transport chain, optimal use of available capacity) (R&I need: access to / exit from the airport: airports are obvious multimodal nodes for aviation).
• Advanced techniques for passenger flow prediction. This element is aimed at developing advanced predictive models to anticipate the evolution of an airport’s passenger flows within the day of operations and assess the operational impact on both airport processes and the ground transport system, with the aim of enabling real-time CDM between airports and ground transport stakeholders and enhanced passenger information services (R&I need: passenger experience at the airport).
• Traveller behaviour analysis, modelling and simulation. This element investigates the need for new/different big data sources for the analysis of multimodal travel behaviour (including requirements for integrated, private data management, so that all service providers can sell capacity into an integrated booking system but retain their own supply privacy), the need for better representation of multimodal trips in transport and traffic simulation models, the integration of commercially sensitive data from air and ground transport operators into passenger demand models (through, for example, federated ML models) and the impact of the shift from feeder flights to other modes of transport (environment, door-to-door time, better resource allocation, freeing up airport slots, etc.) (R&I need: passenger experience at the airport).
• Passenger travel behaviour and requirements. This research covers the factors affecting passenger mode choice, especially the factors driving the decision to use a particular mode for different distance segments (e.g. air versus rail for short-haul traffic and the complementarity between them), preferences relating to the journey (travel time, comfort, price, CO₂ emissions) and how these preferences will affect future door-to-door journeys. In addition, passengers’ journey planning has to be considered: how to improve the door-to-door options and information on multimodal travel for passengers (e.g. through offering one ticket for multimodal trips (single ticketing), mobility as a service tickets, one-stop shops, etc.) (R&I need: passenger experience at the airport).
• Integrated performance network. This element covers the establishment of an overall transport network performance framework to improve passenger experience and planning, through improved collaboration between different modes of transport; improved integration of data and processes; specification of GDPR-compliant requirements for the collection, analysis and exploitation of additional data. The aim is to create a pan-European database supporting journey optimisation and personalisation of offers to customers. The research will also address information for passengers during disruption (e.g. enabling passengers to rebook and replan during disruption by further developing existing tools); corresponding passenger rights information; and real-time, user-friendly, accessible and accurate information to improve the passenger experience before and during travel (R&I need: an integrated transport network performance cockpit).
• Multimodal performance scheme. The aim is to develop a set of multimodal KPIs – based on the current single European sky performance scheme, ICAO, EU connectivity indicators and indicators used in other modes of transport – to allow the evaluation of the impact of innovative intermodal transport solutions on the quality, efficiency and resilience of the door-to-door passenger journey. Tactically, there is a need to move away from less meaningful passenger-centric metrics such as small average delays, instead producing better measures of significant disruption (e.g. longer delays, missed connections, denied boarding, cancellations) and integrating currently lacking metrics into the KPAs of flexibility and predictability, which remain important dimensions in travel choice decision-making and trade-offs for the passenger. A better understanding of passenger (dis)utility and passenger archetypes is required to deliver better service to the customer (R&I need: an integrated transport network performance cockpit).