Bridging the gap between intelligent transportation systems development and state decision-making

This study addresses the ongoing disconnect between the rapid advancement of Intelligent Transportation Systems (ITS) and their limited integration into transport policy and decision-making in Latvia. Despite global ITS progress, adoption at national levels remains inconsistent, often due to structural, institutional, and educational barriers. A mixed-methods approach was used, comprising (1) a systematic literature review of 57 open-access sources to identify global ITS trends, (2) a comparative case study of Latvia’s ITS ecosystem against European and international best practices, and (3) participatory action research with public authorities, industry associations, and academic institutions to co-develop practical insights. Findings informed the redesign of the International Transport Management course at Riga Technical University (RTU), addressing gaps that limit ITS adoption in governance. In the next phase, the revised curriculum was piloted alongside stakeholder interviews, and a data-driven Transport Intervention Modelling (TIM) system was developed on RTU’s High-Performance Computing platform. The TIM system supports both education and ITS development for policy and industry. The study concludes that aligning ITS with governance requires both technical and institutional coordination. Education reform, when embedded in a multi-stakeholder environment, can bridge the gap between innovation and policy. The framework and interventions piloted in Latvia offer a scalable model for integrating ITS into strategic transport governance elsewhere.

Authors

Publication Details

Journal
International Journal of Innovative Research and Scientific Studies
Published
2025-09-19
DOI
https://doi.org/10.53894/ijirss.v8i6.10101
Primary Topic
Advanced Research in Systems and Signal Processing
Type
article
Field-Weighted Citation Impact
0.00
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article

Bridging the gap between intelligent transportation systems development and state decision-making

Justīna Hudenko, Ingūna Jurgelāne-Kaldava, Liga Millere-Kruma
International Journal of Innovative Research and Scientific Studies
Advanced Research in Systems and Signal Processing
article

Bridging the gap between intelligent transportation systems development and state decision-making

Justīna Hudenko, Ingūna Jurgelāne-Kaldava, Liga Millere-Kruma
article en

Abstract

This study addresses the ongoing disconnect between the rapid advancement of Intelligent Transportation Systems (ITS) and their limited integration into transport policy and decision-making in Latvia. Despite global ITS progress, adoption at national levels remains inconsistent, often due to structural, institutional, and educational barriers. A mixed-methods approach was used, comprising (1) a systematic literature review of 57 open-access sources to identify global ITS trends, (2) a comparative case study of Latvia’s ITS ecosystem against European and international best practices, and (3) participatory action research with public authorities, industry associations, and academic institutions to co-develop practical insights. Findings informed the redesign of the International Transport Management course at Riga Technical University (RTU), addressing gaps that limit ITS adoption in governance. In the next phase, the revised curriculum was piloted alongside stakeholder interviews, and a data-driven Transport Intervention Modelling (TIM) system was developed on RTU’s High-Performance Computing platform. The TIM system supports both education and ITS development for policy and industry. The study concludes that aligning ITS with governance requires both technical and institutional coordination. Education reform, when embedded in a multi-stakeholder environment, can bridge the gap between innovation and policy. The framework and interventions piloted in Latvia offer a scalable model for integrating ITS into strategic transport governance elsewhere.

International Journal of Innovative Research and Scientific StudiesVol. 8(6)
Openalex Percentile: Top 68%
Advanced Research in Systems and Signal Processing
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