Methodological objectivesBusiness / economic objectivesLegal and institutional objectivesSocial objectivesPolicy objectives
To build and maintain the first organized collection of use cases for Big Data applications and services in the field of transport and logistics.
NOESIS has assigned a dedicated task (WP2 – Task 2.4) for the development of the 1st collection of Big Data use cases in Transport, the Big Data in Transport Library (BDTL). The BDTL will constitute a reference point as for the first time Transport Challenges will be associated with Big Datasets, Big Data applications and the potential value anticipated.
To investigate the pattern(s) behind the success of big data services/investments in terms of value generation
In WP3, an ex-post analysis and assessment of various big data services and schemes based on existing experiences in different transport sectors and geographical areas as well as their classification with respect to their scores in specific KPIs, defined in Task 5.1, will be carried out (D3.2). This analysis will lead to the development of the first (to our knowledge) Decision Support Tool for evaluating big data technologies in transport.
To identify the methodological issues and to develop appropriate tools in order to allow for effective data mining and data exploitation for transport related challenges.
NOESIS will consolidate the methods commonly used for data handling from a variety of fields and examine its usefulness to transportation, including data storing, modeling and visualization (D2.2). In WP2 NOESIS will explicitly describe the already available transportation-related datasets on a global level. Taxonomy of the commonly used parameters in transport for the representation of transportation and logistics systems, policy making and operations will be developed (D2.1). This exercise will include identifying and mapping the implementation context of big data in transportation (D2.3).
To develop an Impact Assessment methodology for assessing the socioeconomic impact of Big data applications.
In WP5 of NOΕSIS an ex-ante (forward) assessment of the potential of Big Data in transport investments and the related services will be conducted (including innovative business model schemes still in a pilot phase) by identifying a set of KPI‘s and their respective value ranges (scores). KPI‘s will be in line with the EU policy objectives for an efficient, data- driven, environmentally friendly and intermodal transport system. Further at D5.1, an Impact Assessment methodology (IAM) for the translation of the KPI‘s scores of the various big data applications to socio-economic value will be developed
To create new business opportunities for SMEs of the new Economy in providing high quality and low cost, big data applications in transport.
At D5.2, NOESIS will provide a set of recommendations for the right implementation of successful business models for using of big data for transport. The analysis will make recommendations for both freight and passenger transport depending on the specific characteristic of each transport mode, or the interconnection of different transport modes.
To create the Responsible Code of conduct for big data management in transport
WP4 will assess possible areas of misuse and potential danger that arise with the implementation of big data generation and technologies in the field of transport. This will be performed explicitly by identifying and addressing the potential privacy and security concerns. Techniques and procedures successfully implemented in other fields in relation to big data will be described and adjusted in order to address the big data issues in transport. Guidelines with the use of the adopted techniques as well as information concerning ethics and data privacy laws will be produced in a “Code of Conduct” form.
To develop a methodology for assessing the socio-economic value of Big Data applications, in order to boost social inclusion in service provision and opportunities throughout Europe
The first step within NOESIS project to boost the social inclusion for Big Data application is the online Big Data in Transport Library (BDTL) developed. The BDTL will be developed as an open and easily accessible website which will serve as a knowledge hub where use cases and relevant findings will be stored and accessed by all users (D6.1). Additionally, and aiming towards organization reformations that might be required to foster social inclusion, D5.3 and D6.4 will develop concrete policy recommendations as key outputs for the NOESIS project in order to generate a societal impact beyond the research carried out in the project.
To provide know-how and methodological ideas to big data domains outside transport for the transfer of the methodological approach
NOESIS website and social networking presence will be the major project dissemination tools. (Facebook, Twitter and Linked-in). Further Creation of a Network of Stakeholders (NoS) will be one of the main tools for NOESIS to disseminate know-how outside transport domains (D6.1-D6.6) and specifically to the Big Data/Data science community.
To provide an integrated policy analysis tool in respect to big data implementation
NOESIS will develop a Handbook on Key Lessons Learnt and Transferable Practices that will provide information on best practice of Big Data implementation in transport in Europe, , as part of D4.3.