A big data modeling approach with graph databases for SPAD risk

Rawia El Rashidy, Peter Hughes, Miguel Figueres Esteban, Chris Harrison, Coen Van Gulijk

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)


This paper proposes a model to assess train passing a red signal without authorization, a SPAD. The approach is based on Big Data techniques so that many types of data may be integrated, or even added at a later date, to get a richer view of these complicated events. The proposed approach integrates multiple data sources using a graph database. A four-steps data modeling approach for safety data model is introduced. The steps are problem formulation, identification of data points, identification of relations and calculation of the safety indicators. A graph database was used to store, manage and query the data, whereas R software was used to automate the data upload and post-process the results. A case study demonstrates how indicators have extracted that warning in the case that the SPAD safety envelope is reduced. The technique is demonstrated with a case study that focuses on the detection of SPADs and safety distances for SPADs. The latter provides indicators for to assess the severity of near-SPAD incidents.

Original languageEnglish
Pages (from-to)75-79
Number of pages5
JournalSafety Science
Issue numberPart B
Early online date6 Dec 2017
Publication statusPublished - 1 Dec 2018


Dive into the research topics of 'A big data modeling approach with graph databases for SPAD risk'. Together they form a unique fingerprint.

Cite this