Coordinated by Indra, Spain, Transforming transport is intended to be a tangible demonstration of how data generated by the transport and logistics sector can be exploited in an innovative way using big data technologies to improve efficiency and the management of services and develop new sources of revenue or business models.
It is one of the largest projects funded by the European Commission (EC) within its Horizon 2020 programne, both in terms of its €18.7m budget and the fact that it has 47 partners from Britain, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, and the Netherlands, including PTV and Fraunhofer Germany, Thales, infrastructure managers Adif, Spain, and Network Rail, Britain, and several universities.
The pilot projects will be used to develop and test new algorithms based on existing big data technologies that allow for the integration and analysis of real data from diverse sources, developing transport patterns and exploiting these in a way that is most suitable for decision-making.
The Spanish city of Valladolid will implement one of the pilot projects on urban mobility; France, on the connected vehicle; and Britain, on rail transport. The results from the pilot projects are expected to be reusable and replicable, even after the end of the project.
The use of big data is expected to improve operational efficiency by at least 15% by optimising the use of resources and reducing maintenance costs, fuel consumption and incidents. It should also make it possible to offer services better adapted to the clients' needs, while also helping to optimise passenger flows, reduce waiting and freight delivery times, and avoid failed connections between different modes.
Indra, in collaboration with Adif and Ferrovial Agroman, will launch a rail pilot project on the Cordoba - Malaga high-speed line in Spain. Big data technologies will be used to improve the management of the line’s maintenance works, optimise available resources and reduce maintenance costs based on integrating, processing and modelling different data sources including maintenance, assets, traffic data, topology, superstructure data and meteorological information. Real-time predictions will also be made on the impact of maintenance on certain events for rail traffic management.