Periodic Reporting for period 2 - DATASET2050 (Data driven approach for a Seamless Efficient European Travelling in 2050)
Reporting period: 2016-06-01 to 2017-11-30
Freedom of movement within the EU is a fundamental right of European citizens. Improving passenger journeys within Europe boosts EU growth and prosperity. Moreover, it further enhances EU cohesion by increasing tourism, cultural exchanges, economic growth and individual well-being. One of the objectives defined in the Flightpath 2050 vision for the future of European aviation is that "90% of travellers within Europe [taking at least one air segment will be] able to complete their journey, door-to-door within four hours" by 2050. This is known as the 4-hour door-to-door (4HD2D) goal. At the same time, the exponential growth of data collection and connected devices in the digitisation era provides a very favourable setting for data analysis and data-driven decision making. The DATASET2050 Coordination and Support Action (CSA) has analysed available datasets to generate a picture of EU passenger mobility in the door-to-door (D2D) context. The overall tasks and objectives of DATASET2050 are:
(1)To take a passenger-centric approach, defining and measuring several multimodal door-to-door mobility metrics that are interesting and relevant from the passengers' perspective.
(2)To provide useful and valuable quantitative insights into European door-to-door travel through a cutting edge data science approach for both present and future transport scenarios (2035 & 2050), using several multi-modal and social data sources that had previously been in isolated silos.
(3)To assess how the European transport supply profile (capacity, connections, business models, regulations, intermodality, processes, infrastructure) will adapt to the evolution of the demand profile (customers, demographics, passenger expectations, requirements).
(4)To pave the way for a seamless and efficient door-to-door travelling experience in the (near) future, via identifying European transport bottlenecks and improvement areas across the different scenarios, through expert application of state-of-art predictive analytics, modelling, statistical analyses and visualisation.
(5)To identify and forecast novel multimodal transport concepts and innovations supporting the improvement of door-to-door metrics.
(1)To take a passenger-centric approach, defining and measuring several multimodal door-to-door mobility metrics that are interesting and relevant from the passengers' perspective.
(2)To provide useful and valuable quantitative insights into European door-to-door travel through a cutting edge data science approach for both present and future transport scenarios (2035 & 2050), using several multi-modal and social data sources that had previously been in isolated silos.
(3)To assess how the European transport supply profile (capacity, connections, business models, regulations, intermodality, processes, infrastructure) will adapt to the evolution of the demand profile (customers, demographics, passenger expectations, requirements).
(4)To pave the way for a seamless and efficient door-to-door travelling experience in the (near) future, via identifying European transport bottlenecks and improvement areas across the different scenarios, through expert application of state-of-art predictive analytics, modelling, statistical analyses and visualisation.
(5)To identify and forecast novel multimodal transport concepts and innovations supporting the improvement of door-to-door metrics.
In DATASET2050, workpackages 2, 3, 4 and 5 document the scientific and technical outcomes with a total of nine deliverables, available on the official website (www.dataset2050.eu). Some of the results are: (please refer to specific deliverables for further information)
Looking at the requirements of the data to feed the DATASET2050 model at all its phases (i.e. door-to-kerb, kerb-to-gate, gate-to-gate, gate-to-kerb and kerb-to-door). Deliverable 2.1
Conducting an intensive review of what data are available, together with analysing temporal/geographical coverage, granularity, cost etc. data acquisition tasks. Deliverable 2.1
Designing and developing a visual tool that enables an easy exploration of the door-to-door datasets, available at: http://visual.innaxis.org/mobilityDataSETs
Developing a data-driven model capable of simulating the door-to-door processes. Deliverable 2.2
Completing the current demand profile, including the current mobility details (passenger behaviour, demographics, passenger expectations, requirements), is reported in Deliverable 3.1 and has provided key input for the corresponding model part (demand). Deliverables 3.1 and 3.2
The current supply profile includes research on the current level of capacity, connections, business models, regulations, intermodality, processes, infrastructure. This is reported on in Deliverable 4.1.
Mobility metrics and indicators have been defined and are described in Deliverable 5.1. These have been grouped into 10 Key Performance Areas (KPAs), generally equivalent to the KPAs defined for Air Traffic Management (ATM) improvement by ICAO. Deliverable 5.1
Metrics calculation using the door-to-door mobility model: focusing on time and price related ones. How long each door-to-door subsegment last depending on passenger profile, how much it costs, what is the current situation compared with strategic agendas (and the 4hour door-to-door objective) Deliverables 5.1 and 5.2
Reachability map (time) representing the overall probability of carrying out a journey from a selected airport within less than a chosen door-to-door duration. Deliverable 5.2
Reachability map (price), in an analogous manner, represent the overall probability of a certain door-door trip costing less than a selected price. Deliverable 5.2
Forecasting future mobility performance and identifying which are the areas with bigger room for improvement in terms of door-to-door performance. Deliverable 5.2
Derivation of passenger-centric performance parameters and the evaluation of new mobility concepts according to these. Deliverable 5.3
Looking at the requirements of the data to feed the DATASET2050 model at all its phases (i.e. door-to-kerb, kerb-to-gate, gate-to-gate, gate-to-kerb and kerb-to-door). Deliverable 2.1
Conducting an intensive review of what data are available, together with analysing temporal/geographical coverage, granularity, cost etc. data acquisition tasks. Deliverable 2.1
Designing and developing a visual tool that enables an easy exploration of the door-to-door datasets, available at: http://visual.innaxis.org/mobilityDataSETs
Developing a data-driven model capable of simulating the door-to-door processes. Deliverable 2.2
Completing the current demand profile, including the current mobility details (passenger behaviour, demographics, passenger expectations, requirements), is reported in Deliverable 3.1 and has provided key input for the corresponding model part (demand). Deliverables 3.1 and 3.2
The current supply profile includes research on the current level of capacity, connections, business models, regulations, intermodality, processes, infrastructure. This is reported on in Deliverable 4.1.
Mobility metrics and indicators have been defined and are described in Deliverable 5.1. These have been grouped into 10 Key Performance Areas (KPAs), generally equivalent to the KPAs defined for Air Traffic Management (ATM) improvement by ICAO. Deliverable 5.1
Metrics calculation using the door-to-door mobility model: focusing on time and price related ones. How long each door-to-door subsegment last depending on passenger profile, how much it costs, what is the current situation compared with strategic agendas (and the 4hour door-to-door objective) Deliverables 5.1 and 5.2
Reachability map (time) representing the overall probability of carrying out a journey from a selected airport within less than a chosen door-to-door duration. Deliverable 5.2
Reachability map (price), in an analogous manner, represent the overall probability of a certain door-door trip costing less than a selected price. Deliverable 5.2
Forecasting future mobility performance and identifying which are the areas with bigger room for improvement in terms of door-to-door performance. Deliverable 5.2
Derivation of passenger-centric performance parameters and the evaluation of new mobility concepts according to these. Deliverable 5.3
DATASET2050 aims to have a socio-economic impact in the field of mobility and transport. From an economic perspective, at present the EU transport industry directly employs around 10 million people and accounts for about 5% of Europe's gross domestic product (GDP). On the social side, DATASET2050's particular impact is achieved by its describing how EU passenger transport performs door-to-door and making suggestions for improving this, putting the passenger at the centre. Improving journeys between any 2 points within Europe boosts EU growth and prosperity, enhances EU cohesion and increases tourism, cultural, personal and economic links between physically distant EU regions.
As well as looking at current door-to-door mobility performance, DATASET2050 also predicts what mobility will be like in the future and where future bottlenecks could be, once the novel mobility concepts have gradually been implemented. This outcome will help decision makers to better foresee and plan necessary investments, adaptation and mobility strategies. All-in-all, in the medium and long terms, DATASET2050's main outcome is contributing to fewer disruptions and smoother, quicker, more comfortable travel for passengers, meeting their individual requirements along the entire door-to-door travel chain.
Some partial milestones and progress achieved beyond the state-of-the-art include:
Calculating, following a data-driven quantitative approach, what the current door-to-door mobility metrics are. DATASET2050 first milestone has been the provision of more advanced quantitative metrics than the simple qualitative analyses / reports available before launching the project.
Accomplishing a better holistic, passenger-centric view of mobility, including future passenger profiling and future transport demand.
Predicting what the bottlenecks in future mobility scenarios will be. This prediction includes assessing and analysing how compressible the current door-to-door sub-segments are; what the potential futuristic scenarios are; what is the relationship between future demand and supply profiles in transport likely to be.
As well as looking at current door-to-door mobility performance, DATASET2050 also predicts what mobility will be like in the future and where future bottlenecks could be, once the novel mobility concepts have gradually been implemented. This outcome will help decision makers to better foresee and plan necessary investments, adaptation and mobility strategies. All-in-all, in the medium and long terms, DATASET2050's main outcome is contributing to fewer disruptions and smoother, quicker, more comfortable travel for passengers, meeting their individual requirements along the entire door-to-door travel chain.
Some partial milestones and progress achieved beyond the state-of-the-art include:
Calculating, following a data-driven quantitative approach, what the current door-to-door mobility metrics are. DATASET2050 first milestone has been the provision of more advanced quantitative metrics than the simple qualitative analyses / reports available before launching the project.
Accomplishing a better holistic, passenger-centric view of mobility, including future passenger profiling and future transport demand.
Predicting what the bottlenecks in future mobility scenarios will be. This prediction includes assessing and analysing how compressible the current door-to-door sub-segments are; what the potential futuristic scenarios are; what is the relationship between future demand and supply profiles in transport likely to be.