<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Understanding the main research directions in transport is crucial to provide useful and relevant insights. The analysis of Horizon 2020, the largest research and innovation framework, has been already realized in a few publications but rarely for the field of transport. Thus, this article is devoted to fill this gap by introducing a novel application of topic modelling techniques, specifically the Latent Dirichlet Allocation (LDA), in the Horizon 2020 framework for transport projects. The method is using the Mallet software with pre-examined code optimizations. As the first step, a corpus is created by collecting 310 project abstracts; afterward, the texts of abstracts are prepared for the LDA analysis by introducing stop words, optimization criteria, the number of words per topics, and the number of topics. The study successfully uncovers the following five main underlying topics: road and traffic safety, aviation and aircraft, mobility and urban transport, maritime industry and shipping, open and real-time data in transport. Besides that, the main trends in transport are identified based on the frequency of words and their occurrence in the corpus. The applied approach maximizes the added value of the Horizon 2020 initiatives by revealing insights that may be overlooked using traditional analysis methods.</p>
</abstract>

Understanding the main research directions in transport is crucial to provide useful and relevant insights. The analysis of Horizon 2020, the largest research and innovation framework, has been already realized in a few publications but rarely for the field of transport. Thus, this article is devoted to fill this gap by introducing a novel application of topic modelling techniques, specifically the Latent Dirichlet Allocation (LDA), in the Horizon 2020 framework for transport projects. The method is using the Mallet software with pre-examined code optimizations. As the first step, a corpus is created by collecting 310 project abstracts; afterward, the texts of abstracts are prepared for the LDA analysis by introducing stop words, optimization criteria, the number of words per topics, and the number of topics. The study successfully uncovers the following five main underlying topics: road and traffic safety, aviation and aircraft, mobility and urban transport, maritime industry and shipping, open and real-time data in transport. Besides that, the main trends in transport are identified based on the frequency of words and their occurrence in the corpus. The applied approach maximizes the added value of the Horizon 2020 initiatives by revealing insights that may be overlooked using traditional analysis methods.

Horizon 2020 Project Analysis by Using Topic Modelling Techniques in the Field of Transport

Budapest University of Technology and Economics (BME), Faculty of Transportation Engineering and Vehicle Engineering (KJK)

Department of Transport Technology and Economics (KTKG) Budapest

Transport and Telecommunication Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Understanding the main research directions in transport is crucial to provide useful and relevant insights. The analysis of Horizon 2020, the largest research...

Horizon 2020 Project Analysis by Using Topic Modelling Techniques in the Field of Transport

Data publikacji: 26 cze 2024

Zakres stron: 266 - 277

DOI: https://doi.org/10.2478/ttj-2024-0019

Słowa kluczowe
Horizon 2020, innovation framework, Latend Diriclet Allocation, topic modelling technique

© 2024 Domokos Esztergár-Kiss, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Horizon 2020 Project Analysis by Using Topic Modelling Techniques in the Field of Transport

Data publikacji: 26 cze 2024

Zakres stron: 266 - 277

DOI: https://doi.org/10.2478/ttj-2024-0019

Słowa kluczoweHorizon 2020, innovation framework, Latend Diriclet Allocation, topic modelling technique

© 2024 Domokos Esztergár-Kiss, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
Horizon 2020, innovation framework, Latend Diriclet Allocation, topic modelling technique