Periodic Reporting for period 2 - NOESIS (NOvel Decision Support tool for Evaluating Strategic Big Data investments in Transport and Intelligent Mobility Services)
Periodo di rendicontazione: 2019-05-01 al 2019-10-31
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 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 outcomes of NOESIS are: a) the Big Data in Transport Library, b) the NOESIS Decision Support tool and c) the NOESIS Impact Assessment Methodology. These tools, together with the Technical and Policy Roadmaps assist in predicting the value generated from potential Big Data investments.
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 outcomes of NOESIS are: a) the Big Data in Transport Library, b) the NOESIS Decision Support tool and c) the NOESIS Impact Assessment Methodology. These tools, together with the Technical and Policy Roadmaps assist in predicting the value generated from potential Big Data investments.
The work focused on building the first organised database of Big Data in transport use cases which contains a list of Big Data technologies or applications and their generated socioeconomic value regarding a transport problem. Following the WP2 action plan the focus was on the setup of the Big Data in Transport Library (BDTL) by collecting relevant use cases. More than 100 use cases have been gathered and are available at the following urls: https://noesis-project.eu/toolbox/ and http://bigdataintransport.eu.
Taking input from WP2, WP3 focused on analysing the BDTL and on developing the NOESIS Decision Support Tool (DST) that by utilising machine learning techniques is able to predict the potential value from future Big Data investments in the transport sector. Since the beginning of WP3, the focus was on analysing the inputs from the BDTL in order to understand the most important features that could be used as input for the Decision Support Tool. After this analysis, the requirements for the DST have been decided and the first version of the DST is available at the following urls: https://noesis-project.eu/toolbox/decision.php and http://www.bigdataintransport.eu/decision.php.
At the same time, in WP5, NOESIS conducted the Data Benefit Analysis (DBA) and an Impact Assessment Methodology (IAM) to appraise Big Data investment intended to optimise the management of transport systems and networks. The first part of the analysis (Data Benefit Analysis) aims at identifying (and if possible) quantifying the value that Big Data applications may provide to the company that promoted it and to society as a whole. Those benefits are included in the “value capture section” of the Big Data in Transport use case template in WP2 to identify the value produced across the different use cases of the BDTL. The validation of the proposed DBA was implemented using the DST to provide insights about the potential value generated by different sort of Big Data applications. The second part of the analysis consist of the design and development of the Impact Assessment Methodology (IAM). The IAM intends to design a set of guidelines aimed at appraise, both ex-ante and ex-post, Big Data investment from the socioeconomic standpoint. NOESIS IAM is based on the combination of Cost-Benefit Analysis (CBA), Multi-Criteria Decision Analysis (MCDA) and sensitivity analysis. The methodology pays especial attention on key aspects such as technological risks and obsolescence. In WP5 NOESIS also identified suitable and successful business models to promote feasible Big Data solutions for public and private stakeholders in managing and optimizing transport systems and networks.
In parallel, in WP4 NOESIS identified legal barriers and constraints related to data protection, data security and data openness in Big Data projects in the transport and logistics sector. Initial research has shown that the biggest challenge for Big Data applications in transport and logistics is data protection law and NOESIS developed guidelines to facilitate the legally compliant use of Big Data applications in transport and logistics. At the same time, NOESIS is providing a set of recommendations on how to design a data governance framework to deal with Big Data in transport organizations to more effectively implement Big Data tools and maximize the rewards for the organizational and institutional point of view.
All the main results of the project are presented in the form of short Policy Briefs that are further supported by Technical and Policy guidelines. This work is based on NOESIS experience gained through the development of the NOESIS Big Data in Transport Library and through an extensive literature review (including also work from other projects).
Taking input from WP2, WP3 focused on analysing the BDTL and on developing the NOESIS Decision Support Tool (DST) that by utilising machine learning techniques is able to predict the potential value from future Big Data investments in the transport sector. Since the beginning of WP3, the focus was on analysing the inputs from the BDTL in order to understand the most important features that could be used as input for the Decision Support Tool. After this analysis, the requirements for the DST have been decided and the first version of the DST is available at the following urls: https://noesis-project.eu/toolbox/decision.php and http://www.bigdataintransport.eu/decision.php.
At the same time, in WP5, NOESIS conducted the Data Benefit Analysis (DBA) and an Impact Assessment Methodology (IAM) to appraise Big Data investment intended to optimise the management of transport systems and networks. The first part of the analysis (Data Benefit Analysis) aims at identifying (and if possible) quantifying the value that Big Data applications may provide to the company that promoted it and to society as a whole. Those benefits are included in the “value capture section” of the Big Data in Transport use case template in WP2 to identify the value produced across the different use cases of the BDTL. The validation of the proposed DBA was implemented using the DST to provide insights about the potential value generated by different sort of Big Data applications. The second part of the analysis consist of the design and development of the Impact Assessment Methodology (IAM). The IAM intends to design a set of guidelines aimed at appraise, both ex-ante and ex-post, Big Data investment from the socioeconomic standpoint. NOESIS IAM is based on the combination of Cost-Benefit Analysis (CBA), Multi-Criteria Decision Analysis (MCDA) and sensitivity analysis. The methodology pays especial attention on key aspects such as technological risks and obsolescence. In WP5 NOESIS also identified suitable and successful business models to promote feasible Big Data solutions for public and private stakeholders in managing and optimizing transport systems and networks.
In parallel, in WP4 NOESIS identified legal barriers and constraints related to data protection, data security and data openness in Big Data projects in the transport and logistics sector. Initial research has shown that the biggest challenge for Big Data applications in transport and logistics is data protection law and NOESIS developed guidelines to facilitate the legally compliant use of Big Data applications in transport and logistics. At the same time, NOESIS is providing a set of recommendations on how to design a data governance framework to deal with Big Data in transport organizations to more effectively implement Big Data tools and maximize the rewards for the organizational and institutional point of view.
All the main results of the project are presented in the form of short Policy Briefs that are further supported by Technical and Policy guidelines. This work is based on NOESIS experience gained through the development of the NOESIS Big Data in Transport Library and through an extensive literature review (including also work from other projects).
NOESIS progresses the state of the art in the Big Data in transport sector in the following ways:
- NOESIS developed 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 are associated with Big Data applications and the potential value anticipated. NOESIS managed to collect more than 100 Big Data in Transport use cases.
- NOESIS developed the first Decision Support Tool for evaluating the socioeconomic impact of Big Data applications in transport. The NOESIS DST is taking as input specific characteristics of the Big Data application into investigation and displays as output the potential benefit of the specific application of the organization and of the society.
- NOESIS prepared a set of guidelines aimed at appraise, both ex-ante and ex-post, Big Data investment from the socioeconomic standpoint. NOESIS Impact Assessment Methodology is based on the combination of Cost-Benefit Analysis (CBA), Multi-Criteria Decision Analysis (MCDA) and sensitivity analysis. The methodology pays especial attention on key aspects such as technological risks and obsolescence.
The above-mentioned tools can assist policy makers, transport industries and transport experts understand the potential and limitations of Big Data applications in transport and take evidence-based decision.
At the same time, NOESIS developed two combined roadmaps (technological and policy-oriented) for the implementation of Big Data in the management and optimisation of transport systems and networks to help policy makers 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. Finally, NOESIS developed policy briefs in order to generate a societal impact beyond the research carried out in the project with the aim 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.
- NOESIS developed 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 are associated with Big Data applications and the potential value anticipated. NOESIS managed to collect more than 100 Big Data in Transport use cases.
- NOESIS developed the first Decision Support Tool for evaluating the socioeconomic impact of Big Data applications in transport. The NOESIS DST is taking as input specific characteristics of the Big Data application into investigation and displays as output the potential benefit of the specific application of the organization and of the society.
- NOESIS prepared a set of guidelines aimed at appraise, both ex-ante and ex-post, Big Data investment from the socioeconomic standpoint. NOESIS Impact Assessment Methodology is based on the combination of Cost-Benefit Analysis (CBA), Multi-Criteria Decision Analysis (MCDA) and sensitivity analysis. The methodology pays especial attention on key aspects such as technological risks and obsolescence.
The above-mentioned tools can assist policy makers, transport industries and transport experts understand the potential and limitations of Big Data applications in transport and take evidence-based decision.
At the same time, NOESIS developed two combined roadmaps (technological and policy-oriented) for the implementation of Big Data in the management and optimisation of transport systems and networks to help policy makers 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. Finally, NOESIS developed policy briefs in order to generate a societal impact beyond the research carried out in the project with the aim 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.