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H2020

BigData4ATM Report Summary

Project ID: 699260
Funded under: H2020-EU.3.4.7.1

Periodic Reporting for period 1 - BigData4ATM (Passenger-centric Big Data Sources for Socio-economic and Behavioural Research in ATM)

Reporting period: 2016-05-09 to 2016-11-08

Summary of the context and overall objectives of the project

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.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

During the first half year of the project (9 May 2016-8 November 2016), the work has focused on:
• producing the Project Management Plan and other management documentation,
• gathering and assessing the different datasets that will be analysed throughout the project,
• conducting a detailed 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.

The management, communication and ethics deliverables produced during the first six months are listed hereafter:
• Project Management Plan (D1.1);
• Data Management Plan (D1.2);
• Project website (D5.1): www.bigdata4atm.eu;
• Ethics deliverables dealing with the Ethics approval for the research activities from the competent authorities (D6.1), the anonymisation of personal information (D6.2), the data sharing agreements (D6.3), the compliance with national and EU law (D6.4), the confirmation of the civilian focus of the research (D6.5) and the appointment of a Data Protection Expert for the project (D6.6).

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, which provides an inventory of the different data available to the project, analysing their characteristics (geographical and temporal scope, spatial and temporal resolution, etc.) as well as their strengths and limitations. This document also contains the research questions that will be tackled during the next stages of the project.

Additionally, as an example of application for mobile phone data, an analysis of the Madrid-Barcelona corridor was performed. The objectives of this analysis were to obtain the modal split between airplane, High-Speed Train (HST) and road traffic, the catchment areas of the airport and the HST, and the door-to-door travel times for air transport users. This study was presented at the SESAR Innovation Days that took place in Delft during November 2016.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

After these first six months, 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. In the subsequent stages of the project, the collected datasets will be integrated and analysed to extract new knowledge about passengers’ behaviour, including:
• Door-to-kerb mobility (airport catchment areas, access/egress modes, etc.).
• Kerb-to-gate mobility patterns.
• Expenditure patterns.
• Passengers' opinions and sentiments with respect to ATM.

The project will evaluate the potential of this information to improve the quality of decision-making in air transport and ATM. 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 any of the stakeholders of the aviation sector, such as airlines and airports.
• 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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