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Passenger-centric Big Data Sources for Socio-economic and Behavioural Research in ATM

Periodic Reporting for period 3 - BigData4ATM (Passenger-centric Big Data Sources for Socio-economic and Behavioural Research in ATM)

Reporting period: 2017-05-09 to 2017-11-08

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.
During the first 18 months of the project, the work has focused on:
• producing the Project Management Plan and other management documentation,
• gathering and assessing the datasets that will be analysed throughout the project,
• conducting a literature review and a consultation process with a variety of ATM stakeholders, in order to define the research questions that will be addressed during the remaining of the project,
• completing the data analysis and modelling work;
• launching the first two case studies.

The management, communication and ethics deliverables produced are:
• Project Management Plan (D1.1);
• Data Management Plan (D1.2);
• Project website (D5.1):;
• Ethics deliverables.

The work dealing data acquisition and quality assessment has been documented in deliverable D2.1 Inventory and Quality Assessment of Data Sources for ATM Socioeconomic and Behavioural Studies. The main contents of D2.1 are:
• Data quality factsheets, which describe the characteristics of different data sources (quality, spatial and temporal resolution, etc.)
• A review of the data sources traditionally used for socio-economic and behavioural studies in ATM.
• A review of the different ‘Big Data’ sources that will be explored during the BigData4ATM project.
• A set of research questions to be investigated within the BigData4ATM project.

The data analysis and modelling work has been documented in D3.1 Analysis of Passenger Behaviour from ICT-based Geolocation Data. The following tasks have been carried out:
• Development of methodologies and algorithms to infer activity-travel patterns at different scales. Due to their worldwide coverage, Twitter data have been used to reconstruct international passenger flows at a more aggregated level. Mobile phone records, which provide bigger samples and higher temporal granularity, have been used to reconstruct in a more detailed manner the airport access/egress legs in those countries where mobile phone data are available.
• Development of methodologies to extract intra-airport mobility indicators, such as heat and flow maps, from Twitter data.
• Development of methodologies of methodologies to carry out opinion and sentiment analysis from Twitter data and extract indicators related to passenger satisfaction levels.
• Development of methodologies to extract information from credit card transactions data. This information includes passenger expenditure (both inside and outside the airport), comparison between different airports and seasons, and the impact of disruptions in ATM on airport non-aeronautical revenues.
• Development of statistical approaches and data fusion algorithms to upscale the observed behaviour to the total population.
• Comparison of the information extracted from non-conventional data with that available from other sources (e.g. surveys), in order to validate the newly developed methods.
• Development of a tool to visualise and calculate airport accessibility indicators both by public transport and private car.
BigData4ATM has analysed the main data available for socioeconomic and behavioural research in ATM. This work, documented in deliverable D2.1 will be helpful for other projects in order to select the data necessary for their research. The collected data have been integrated and analysed to extract new knowledge about passengers’ behaviour, including door-to-kerb and kerb-to-gate mobility patterns, expenditure patterns, and passengers' opinions and sentiments. This work is documented in deliverable D3.1.

During the last phase of the project, the methodologies developed during the analysis and modelling stage will be put into practice through a set of case studies, with the aim of demonstrating the advantages of Big Data as a source of ATM passenger-related information. The outcomes of this work are expected to have a positive impact on the ATM system at several levels, such as:
• The design of more agile ATM system designs, that are more resilient to challenges such as rapid changes in demand or disruptive events, thanks to the ability to evaluate ATM performance through the impact on passengers.
• A seamless integration of ATM into the transport network.
• The use of new metrics and management decisions that are driven by passenger needs.

Despite being of an eminently exploratory nature, the research activities proposed by BigData4ATM entail a significant innovation potential and can open new market opportunities in several areas:
• New analytics products and services replacing or complementing the traditional methods used to gather information on passenger behaviour. These applications go beyond ATM, and can be of interest for all the stakeholders of the aviation sector.
• Innovative demand forecasting methodologies and tools. Here again, there is a clear applicability in the context of ATM, but also in other areas related to aviation business intelligence and more generally to socioeconomic research.
• New decision-support tools helping the ATM sector better respond to passenger needs and expectations. Particularly interesting are the opportunities to develop crisis management tools based on a more comprehensive knowledge of passengers’ reactions to ATM disruptions.
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