The planning and management of transport systems requires accurate, reliable and updated travel demand information, including travellers’ socio-economic profile, origin and destination of the trips, trip purpose, transport modes and route choice. Traditional data collection methods, such as household travel surveys, are expensive and require months or even years to complete. TripOD is an innovative solution developed by Nommon that mines and analyses a variety of ICT-based data sources (e.g., mobile phone records, smart cards), alone or in combination with traditional data (e.g., census, traffic counts), to provide frequently updated and rich demographic and travel demand information in a fraction of the time and cost required by traditional methods. The targeted users are those stakeholders in need of travel demand data, including public authorities and agencies in charge of transport planning and management, public and private transport operators and service providers (e.g., toll road concessionaires), consultants and researchers. Altogether, these users constitute a market of significant size, which is expected to grow continuously along the coming decades due to the increasing awareness about the importance of sustainable and efficient transport systems and the cost-efficiency of the TripOD solution, which makes it affordable for public bodies and companies of any size. The overall goal of the feasibility study is to assess the technological and economic viability of the proposed concept and elaborate a detailed business plan. The specific objectives are: - to evaluate the technical feasibility of the solution in operational conditions; - to provide an in-depth analysis of the market at worldwide level, including users and their needs, competitors and entry barriers, data providers, stakeholders and potential alliances; - to evaluate financial feasibility; - to design a strategy to protect intellectual property rights; - to identify risks and opportunities.

The provision of ATM services has for a long time been a national monopoly, with the important role attributed to the nation state (by ICAO) of managing air traffic safely in its national airspace. In addition, provision of air traffic services (ATS) has traditionally been considered a natural monopoly due to the large infrastructure investments. These elements are now both changing. The European Union is increasingly entering the air traffic management world, opposing the national character of the industry and increasing the scale at which air traffic services could be provided. In addition, technological changes are more and more reducing the need for large scale ground-based infrastructure and expensive equipment, putting into question the natural monopoly character of the industry. Therefore today is the right time to study the liberalization and introduction of competition in the ATM sector. However, competition can be introduced at various levels and in different ways. The overall goal of COMPAIR is to investigate how to introduce competitive incentives in the ATM sector so as to best contribute to the achievement of the European high-level policy objectives for aviation. The project will pursue the following objectives: 1. propose a set of new institutional market designs for the introduction of competition in the European ATM sector; 2. define a framework allowing a comprehensive assessment of the impact of different institutional market designs on ATM stakeholders and society at large; 3. develop a variety of economic and network simulation models allowing the evaluation of the proposed regulatory approaches along the dimensions identified as relevant in the assessment framework; 4. assess the feasibility and acceptability of proposed institutional changes for various market actors; 5. propose a vision and derive policy recommendations for the implementation of those new institutional structures identified as most beneficial for the European ATM system.

The Flightpath 2050 report envisages a passenger-centric air transport system thoroughly integrated with other transport modes, with the goal of taking travellers from door to door predictably and efficiently. However, ATM operations have so far lacked a passenger-oriented perspective, with performance objectives not necessarily taking into account the ultimate consequences for the passenger. There is a lack of understanding of the impact of passengers’ behaviour on ATM and vice versa. Research in this area has so far been constrained by the limited availability of behavioural data. The pervasive penetration of smart devices in our daily lives and the emergence of big data analytics open new opportunities to overcome this situation: for the first time, we have large-scale dynamic data allowing us to test hypotheses about travellers’ behaviour. The goal of BigData4ATM is to investigate how these data can be analysed and combined with more traditional demographic, economic and air transport databases to extract relevant information about passengers’ behaviour and use this information to inform ATM decision making processes. The specific objectives of the project are: 1. to integrate and analyse multiple sources of passenger-centric spatio-temporal data (mobile phone records, data from geolocation apps, credit card records, etc.) with the aim of eliciting passengers’ behavioural patterns; 2. to develop new theoretical models translating these behavioural patterns into relevant and actionable indicators for the planning and management of the ATM system; 3. to evaluate the potential applications of the new data sources, data analytics techniques and theoretical models through a number of case studies, including the development of passenger-centric door-to-door delay metrics, the improvement of air traffic forecasting models, the analysis of intra-airport passenger behaviour and its impact on ATM, and the assessment of the socio-economic impact of ATM disruptions.

ATM performance results from the complex interaction of interdependent policies and regulations, stakeholders, technologies and market conditions. Trade-offs arise not only between KPAs, but also between stakeholders, as well as between short-term and long-term objectives. While a lot of effort has traditionally been devoted to the development of microscopic performance models, there is a lack of useful macro approaches able to translate local improvements or specific regulations into their impact on high-level, system-wide KPIs. The goal of INTUIT is to explore the potential of visual analytics, machine learning and systems modelling techniques to improve our understanding of the trade-offs between ATM KPAs, identify cause-effect relationships between KPIs at different scales, and develop new decision support tools for ATM performance monitoring and management. The specific objectives of the project are: 1. to conduct a systematic characterisation of the ATM performance datasets available at different spatial and temporal scales and evaluate their potential to inform the development of new indicators and modelling approaches; 2. to propose new metrics and indicators providing new angles of analysis of ATM performance; 3. to develop a set of visual analytics and machine learning algorithms for the extraction of relevant and understandable patterns from ATM performance data; 4. to investigate new data-driven modelling techniques and evaluate their potential to provide new insights about cause-effect relationships between performance drivers and performance indicators; 5. to integrate the newly developed analytical and visualisation functionalities into an interactive dashboard supporting multi-dimensional performance assessment and decision making for both monitoring and management purposes.