Improving Railway Maintenance Actions with Big Data and Distributed Ledger Technologies

  • Roberto SpigolonEmail author
  • Luca Oneto
  • Dimitar Anastasovski
  • Nadia Fabrizio
  • Marie Swiatek
  • Renzo Canepa
  • Davide Anguita
Conference paper
Part of the Proceedings of the International Neural Networks Society book series (INNS, volume 1)


Big Data Technologies (BDTs) and Distributed Ledger Technologies (DLTs) can bring disruptive innovation in the way we handle, store, and process data to gain knowledge. In this paper, we describe the architecture of a system that leverages on both these technologies to better manage maintenance actions in the railways context. On one side we employ a permissioned DLT to ensure the complete transparency and auditability of the process, the integrity and availability of the inserted data and, most of all, the non-repudiation of the actions performed by each participant in the maintenance management process. On the other side, exploiting the availability of the data in a single repository (the ledger) and with a standardised format, thanks to the utilisation of a DLT, we adopt BDTs to leverage on the features of each maintenance job, together with external factors, to estimate the maintenance restoration time.


Big data analytics Distributed Ledger Technologies Railway maintenance actions 



This research has been supported by the European Union through the projects IN2DREAMS (European Union’s Horizon 2020 research and innovation programme under grant agreement 777596).


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Roberto Spigolon
    • 1
    Email author
  • Luca Oneto
    • 2
  • Dimitar Anastasovski
    • 1
  • Nadia Fabrizio
    • 1
  • Marie Swiatek
    • 3
  • Renzo Canepa
    • 4
  • Davide Anguita
    • 2
  1. 1.Cefriel - Politecnico di MilanoMilanItaly
  2. 2.DIBRIS - University of GenoaGenoaItaly
  3. 3.Evolution EnergieParisFrance
  4. 4.Rete Ferroviaria ItalianaRomeItaly

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