NOESIS research work started in November 2017 with “WP2 Transport Planning paradigm shift and Big Data Use Cases” and focused on building the first organised database of Big Data Transport Use Cases, (the Big Data in Transport Library) which contains a list of Big Data in transport use cases and their generated socioeconomic value . Each Big Data use case  is assessed through well-defined evaluation criteria and a score regarding the impact to the society and economy.

Following the WP2 action plan that was established at the beginning of the project, WP2 focused on:

  1. The definition of emerging transportation challenges. The results are presented in “D2.1 Big Data and emerging transportation challenges“.
  2. An overview of Big Data technologies and use cases in the transport sector. The results are presented in “D2.2 Big Data implementation context in transport“.
  3. The setup of the Big Data in Transport Library and the collection of relevant use cases that are available at the following url:

NOESIS continued the work in “WP3 Big Data Learning Framework“. WP3 focused on analysing the Big Data in Transport Library from WP2 and on developing the NOESIS Decision Support Tool (DST) that by utilizing machine learning techniques is able to predict the potential value from future Big Data investments in the Transport sector. From the early phases of the project NOESIS consortium worked together to develop an extended use case template that would be useful for the NOESIS DST. Since the beginning of WP3, NOESIS focused on analysing the inputs from the survey in order to understand the most important features that could be used as input for the Decision Support Tool. This work led in developing a shorter version for gathering use cases that is now focused on the most critical features that we need for the Decision Support Tool.

After the analysis of the use cases, the requirements for the Decision Support Tool have been decided. The analysis of the use cases and the requirements of the Decision Support Tool are presented in “D3.1 Learning Framework methodology architecture“.

The final version of the Decision Support Tool is available here: The NOESIS Decision Support Tool is using machine learning techniques to predict the potential value of Big Data investments in Transport. The prediction algorithms are based on a sample of over 100 Big Data in Transport use cases that the NOESIS consortium analysed. To use the Tool you input information on “Transport mode”, “Transport sector”, “Type of data”, “Sample size”, “Operational costs”, “Investment costs”, “Transport challenges”, and you get as output the potential value of your investement. The Tool can be used for pre-screening when considering Big Data in Transport investments and solutions.

In parallel, NOESIS (in “Task 4.1 Legal barriers and constraints“) identified legal barriers and constraints related to data protection, data security and data openness in Big Data projects in the transport and logistics sector. Another objective of this task was to provide know how and transferable solutions implemented in similar industries. The question of data ownership was also addressed. The outcomes of this Task are presented in: “D4.1 Summary to Practitioners on Laws, Regulations, and Directives on Data Privacy, Security and Openness“.

Big Data is quite a recent phenomenon, and, because of that, there has not been enough time yet to design a rigorous institutional framework to ensure the rights and obligations of the stakeholders in the use of the information. “Task 4.2 Institutional and governmental issues” addressed the following points:

  • Analysis of the organizational and governance aspects implemented in similar industries that are worth to be replicated in the use of Big Data for transport.
  • Identification of regulation and governance issues such as: monopolistic power in the ownership of data and the subsequent need to regulate prices in some cases; information quality safeguarding and the subsequent need to promote a state own-entity to ensure quality; privacy and data openness, etc.
  • Design of the governance model to deal with Big Data for transport.

Task 4.2 started on M13 (November 2018) and the results were presented in “D4.2 Data governance and institutional issues“.

The goal of “Task 4.3 Key lessons learnt and transferable practices” was to investigate the key lesson learnt from the Big Data use cases derived from the NOESIS Big Data in Transport Library. Task 4.3 started on M16 and the outcomes are presented in “D4.3 Handbook on Key Lessons Learnt and Transferable Practices“. The purpose of this Handbook is to use knowledge derived from the analysis of NOESIS use cases to improve the outcome of the Big Data implementation in transport and to preclude the potential undesirable effects of these processes.

Task 5.1 Impact assessment of big data for transport” conducted the Data Benefit Analysis (DBA) and the Impact Assessment Methodology (IAM) to appraise Big Data investment intended to optimize the management of transport systems and networks. The results of this task 5.1 are described in deliverable “D5.1 Data Benefit Analysis and Impact Assessment Methodologies (IAM) for appraising big data solution in transport“.

Task 5.2 Business and organizational models” started on M16 (February 2019) and its main objective was to identify and propose potential business models to promote feasible Big Data solutions for public and private stakeholders in managing and optimizing transport systems and networks. The outcomes of this task are presented in “D5.2 Suitability of business and organizational models for the successful implementation of big data in transport solutions“.

Task 5.3 Technological and policy roadmaps for Big Data investments in Transport” defined technological and policy roadmaps intended to promote the rational implementation of Big Data for managing and optimizing transport networks and systems in Europe. The aim of Task 5.3 was to help policy makers and managing directors of transport companies to better know the right steps to go ahead in the implementation of Big Data solutions for adding social value through the use of successful business models. The results of Task 5.3 are included in the deliverable “D5.3 Technological and policy roadmaps“.

Finally, NOESIS  pulled together the outputs and conclusions from all the work packages and developed Policy Briefs  for distribution to policy makers on EU/national/regional level. Based on the research conducted, the experience gained through the development of the NOESIS Big Data in Transport Library and on a series of workshops conducted, a set of policy briefs were developed to provide recommendations to cities, transport operators, academia, industry, and the EC on an integrated view on opportunities, challenges and limitations of applications of Big Data in transport. The policy recommendations are presented in “D6.5 Policy Briefs”.

The document begins by presenting the main obstacles with regards to the implementation of Big Data solutions in the transportation sector:

  • Lack of skilled technical personnel to work with data for Transport Authorities
  • Lack of data collaboration and sharing among institutions
  • Lack of understanding between transportation experts and data scientists
  • Lack of access to different data sources and data variability
  • Lack of standards in databases
  • Lack of data quality
  • Lack of infrastructure for storage
  • Cost of purchasing the data
  • Potential of open data not fully realized
  • Uncertainty about data ownership regulation
  • Uncertainty on data privacy and protection law
  • Uncertainty in the benefits provided by the investment in Big Data
  • Lack of financing or business models

The key policy recommendations that the document discusses are the following:

  • Skilled personnel within transport authorities and companies
  • Promoting cooperation among stakeholders
  • Transferability of Big Data “know-how”
  • Improve the quality of data
  • Opening data to boost the economy and research activities
  • Ensuring data protection compliance and supporting the implementation of the GDPR
  • Understand the benefits of big data solutions to better incorporate them in transport
  • Encouragement of feasible business models and access to financing

In case you want more information, feel free to send contact us at: info (at) noesis-project . eu