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Coordination and support Action for Mobility in Europe: Research and Assessment

Periodic Reporting for period 2 - CAMERA (Coordination and support Action for Mobility in Europe: Research and Assessment)

Reporting period: 2019-05-01 to 2020-10-31

"The ever-increasing demand for mobility, particularly for aviation, that continued for the first two decades of new century and saw demand for flights increase by around 4% per annum until 2019, was brought to a shuddering halt by the coronavirus of 2020. In 2019 there were 4.5 billion air passengers worldwide, more than double the numbers for 2006. In Europe, EUROCONTROL's current forecast (December 2020) predicts that, if the new Corvid-19 vaccines are effective in 2021, traffic will be back to normal by 2024. The 4% annual increase in demand will continue from then, though in a much changed environment.
Aviation provides the connectivity necessary for European integration, and is vital for the economic growth of the European Union (EU). According to the EU, ""aviation supports close to 5 million jobs and contributes €300 billion, or 2.1% to European GDP"". Aviation's research and development produces one of the best returns on investment and provides impetus to many other industries. The EU's funding for aviation research is over €5bn and this must be optimised to properly address the needs of European citizens. Therefore, it is important to identify gaps and challenges to ensure the sustainable development of the European air transport system.
CAMERA is a Coordination and Support Actions (CSA), designed to investigate research initiatives from the past decade that focus on the European air transport system and its integration with other transport modes, focusing on customer experience. The whole door-to-door chain of a typical passenger's journey, including access and egress by other forms of transport, must be viewed holistically to ensure that this journey can be undertaken efficiently and comfortably, at optimised cost and in the shortest possible time. Unused time - generally buffers left to account for unpredictable events - must be minimised. This is especially important in today's age of artificial intelligence, increased connectivity and personalised services. CAMERA takes this entire journey as its focus, therefore, while keeping air transport at the heart of an integrated, environmentally friendly and efficient transport system.
CAMERA is assessing these past and ongoing projects to uncover how they have contributed to the ""Flightpath 2050"" goal of an ""integrated seamless, energy efficient, diffused intermodal system taking travellers and their baggage from door-to-door, safely, affordably, quickly, smoothly, seamlessly, predictably and without interruption"" by 2050.
CAMERA's analysis provides the basis for future decisions on where EU research funding should be targeted to address the mobility needs of European citizens, so that future generations can benefit from reliable, efficient, resilient, safe and sustainable transport systems. The results of this analysis are published each year in CAMERA's Annual Mobility Report."
CAMERA has developed its Performance Framework to define the five mobility layers, the metrics, KPAs and KPIs that have been designed to measure the success of European research in achieving the mobility challenges established in Flightpath 2050.
The final CAMERA natural-language processing model was used to analyse over 40,000 projects from EU-funded Horizon 2020 and FP7 projects, since 2007. Using this model, CAMERA selected 926 mobility-related projects for macro-modelling and analysis. This model was validated by the project Advisory Board in June 2019.
The selected projects were analysed using the CAMERA macro-modelling methodology that has been further advanced in this reporting period. This methodology is based mainly on text mining and natural language processing techniques, with tools from data analytics, statistical analysis and data visualisation. It allowed a semi-automatic assessment of the chosen projects, providing nine different high-level classes of mobility research topic. Specific metrics were developed to allow the projects to be assigned to the five mobility layers and the key performance areas (KPAs) that are described in the CAMERA performance framework. Further analysis showed how funds were distributed across the different thematic research areas, and to European countries' research institutions. Again, these results have been validated by expert-based qualitative assessment.
Research gaps were identified by combining this quantitative assessment and a qualitative assessment through consultation with the Advisory Board and expert-based analysis of the metrics obtained. An interactive dashboard, work on which started this period, will enable these gaps to be visualised and allow identification of further gaps and bottlenecks through detailed inspection of the results.
These models and the results of their analyses were presented in the 2nd and 3rd CAMERA mobility reports that were delivered. The micro-modelling methodology to be used during the final reporting period has now been designed, but not yet implemented.
CAMERA's work has continued to be performed in close collaboration with Working Group 1 of ACARE. An invited session was also jointly organised at the TRA2020 conference in June 2020 with the RINGO and OPTICS2 CSAs, where the project and its results were presented and used as the basis for round-table discussions.
"CORDIS is the EC's primary public repository and portal for disseminating information on all EU-funded research projects and their results. It currently includes texts from just over 40,000 projects from the FP7 and Horizon 2020 programmes, many of which are unclassified and unstructured, rendering traditional analysis techniques difficult and ineffective. CAMERA has designed an innovative AI-based methodology to overcome this problem. Natural language processing (NLP) techniques, supported by expert-based assessment and validation, have been applied to implement a system that can effectively extract crucial semantics from these CORDIS texts and thereby enable projects to be classified into various categories.
CAMERA has experimented with a semi-supervised text classification technique based on a latent Dirichlet allocation algorithm. This technique can detect underlying patterns in textual data and, based on these patterns, group the text documents analysed into separate categories, without needing any human input. In the first stage, the mobility-relevant projects from the full set of CORDIS projects were identified, filtered and entered in the second stage, where they are automatically classified into nine different groups. Furthermore, the abstracts and summaries of the extracted projects were analysed semantically to assess the status of the key performance areas (KPAs) and their respective key performance indicators (KPIs) as defined in the CAMERA performance framework. The automatic analysis used a keyword-based approach defined by CAMERA experts. As a result, a series of metrics were generated and used to present the assessment of the mobility projects against the different KPAs/KPIs for the different mobility layers that reflect the Flightpath 2050 objectives. Finally, in the final year of the project, work is underway on more robust and refined models and analytics, so that, with the help of expert assessment, a unique automatic ""human in the loop"" project evaluation methodology will be developed that can provide a detailed analysis of the state of mobility research, together with a set of recommendations for future research."
2nd CAMERA WS
Concept map
Distribution of FP7-H2020 mobility funds accross time
CAMERA Performance Framework
Topic modelling process
Geographical distribution of FP7-H2020 mobility funds
Aviation topic modelling cloud