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 journalArticle

3 Citations (Scopus)

Abstract

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.

LanguageEnglish
Pages75-79
Number of pages5
JournalSafety Science
Volume110
Issue numberPart B
Early online date6 Dec 2017
DOIs
Publication statusPublished - Dec 2018

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Data structures
Databases
Safety
Information Storage and Retrieval
Research Design
Software
SPAD
Big data
authorization
incident
event

Cite this

El Rashidy, Rawia ; Hughes, Peter ; Figueres Esteban, Miguel ; Harrison, Chris ; Van Gulijk, Coen. / A big data modeling approach with graph databases for SPAD risk. In: Safety Science. 2018 ; Vol. 110, No. Part B. pp. 75-79.
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A big data modeling approach with graph databases for SPAD risk. / El Rashidy, Rawia; Hughes, Peter; Figueres Esteban, Miguel; Harrison, Chris; Van Gulijk, Coen.

In: Safety Science, Vol. 110, No. Part B, 12.2018, p. 75-79.

Research output: Contribution to journalArticle

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