Knowledge gaps and new Big Data innovation sources in freight and logistics

The freight and logistics sector experiences a period of great transformation as new technological options such as zero emission vehicles become reality and the digitalization of services proceeds. Proper policies are required to guide and support the transition in an optimal manner. Two reports have recently been published within the STORM project analyzing the ongoing trends and identifying the existing knowledge gaps in the field of freight and logistics.

Many models for analysis of freight transport systems and new market structures in logistics rely on the use of big data analytics. Illustration: Laribat/

In terms of the outputs and insight of freight transport modelling, three important areas were found to lack information and insights. First, there are no plausible projections of how the different aspects of change in logistics will drive the structural change in logistics. Secondly, scenario simulations that are based on the interlinked system changes of new digitalized logistics structures and zero-carbon energy in freight transport should be developed. And last, policy package simulations that will deliver sustainability are missing. Since freight transport is facing non-marginal change, models that can represent processes of structural change will be needed to assess potential points of influence on transport system changes.

Innovations based on Big Data

There is, in fact, a continuous development of new methods and models both for analysis of freight transport systems and new market structures in logistics, and many of these models rely on the use of big data analytics. However, there are a variety of challenges hindering access and utilization of Big Data for freight transport applications due to its nature and unique characteristics, including, but not limited to, data collection, data ownership and accessibility, heterogeneity and standardization, storage, privacy and legal constraints, technical challenges and expertise, quality, validation and representativeness of the data. The lack of awareness or interest in data and data-driven decision-making by senior managers can be a major organizational challenge. Privacy issues often forbid the usage for purposes other than explicitly mentioned in the agreement or contract with the users. Likewise, to share data collected by companies with third parties, individual non-disclosure agreements need to be negotiated which forms a major obstacle in data collaboration and exchange.

Further research is required to improve the possibilities for data collection and preparation, data analytics and utilization, and applications to support decision-making categories. In addition to that, the freight transport models addressing sustainability transitions should be developed in a direction that provides clearer ideas on the upcoming changes instead of offering only general concepts.

To find out more about STORM project, check the project’s website  and sign up for the Newsletter, as well as follow the social media, Twitter and LinkedIn, to be kept updated with the project’s progress and activities.

Text: Yancho Todorov, VTT

Read more:
STORM Deliverable 2.1 Assessment of new needs and knowledge analysis gaps, defining requirements for analysis methods and data.
STORM Deliverable 3.1 Status report on the review of new data sources and methods.


Dr. Yancho Todorov, Project Coordinator
VTT Technical Research Centre of Finland Ltd., Finland

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