Topic outline

  • Project Summary, Objectives and Expected Impacts

    Over the past few years, there has been surge in the interest of the use of big data in the field of transport and logistics. Many crucial elements in creating smart cities, implementing Mobility as a Service (MaaS) as well as promoting mobility innovations (such as Connected and Autonomous Vehicles) are based on the potential that Big Data possess. Despite existing and future promising applications, the critical factors, steps and support environments which lead to a successful application and value generation from Big Data technologies in transport are largely unknown.

    NOESIS (Novel Decision Support tool for Evaluating Strategic Big Data investments in Transport and Intelligent Mobility Services) aims at improving understanding about the impact of big data by developing an integrated, novel and holistic evaluation framework.

    NOESIS conducts an exploration exercise in order to understand the patterns and requirements which are relevant for generating value out of Big Data investments in transport. The key questions NOESIS aims at answering are:

    • how to assess and compare the anticipated benefits from alternative Big Data investments in transport and
    • in which cases should Big Data applications and technologies be implemented to improve transport systems‘ planning and operation?

    The final outcome of NOESIS will be the creation of a Decision Support tool which will be able to predict the value generated (i.e., socioeconomic impact) from Big Data technologies, taking as input the specific characteristics and contextual information of the transport system under evaluation and associating it with a predefined set of use cases with similar characteristics by employing a number of big data (machine learning) techniques.

    NOESIS 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.

  • Presentations, Documents & Leaflets