Contributing to synchromodality through the implementation of a federated data space in Inland Waterway Transport
Artikel i vetenskaplig tidskrift, 2025

Synchromodality aims to enhance freight transport efficiency by the synchronization of intermodal transport elements achieved through accurate, timely, and transparent information exchange, enabling real-time decision-making. Despite advancements in information technology and due to trust issues, data often remains siloed, which hinders synchromodality performance and operational targets. Federated data spaces offer a solution by creating decentralized ecosystems that facilitate the leveraging of critical data. This paper explores the potential of data spaces in the context of synchromodality. Through a case study in inland waterway transport, we demonstrate the practical application of a decentralized, open-source approach, illustrating how data space technologies can enable synchromodal transport. Our findings from the use case indicate that integrating data space technological actors beyond traditional transport stakeholders is essential for successfully implementing synchromodality. These actors can resolve interoperability and data quality issues, enforce data usage policies, and provide applications within the data space to execute needed tasks and services. We conclude that the effectiveness of data space deployment depends on well-defined, robust data usage policies, permitting data exchange among participants under agreed-upon conditions. Lastly, we recommend further research on governance mechanisms, value propositions, and business models for data spaces within the context of synchromodality.

Synchromodality

Federated data space

Data sharing

Inland Waterway Transport

Visibility

Författare

Juan Manuel Pulido

Universiteit Antwerpen

Ivan Dario Cardenas Barbosa

Supply and Operations Management 01

Valentin Carlan

Universiteit Antwerpen

Tom Bergmans

Interuniversity Microelectronics Centre (IMEC)

Thierry Vanelslander

Universiteit Antwerpen

Transportation Engineering

2666691X (eISSN)

Vol. 21 100351

Ämneskategorier (SSIF 2025)

Datavetenskap (datalogi)

DOI

10.1016/j.treng.2025.100351

Mer information

Senast uppdaterat

2025-06-09