A big data modeling approach with graph databases for SPAD risk

• Published in: Safety science Vol. 110; pp. 75 - 79
• Main Authors: Rashidy, Rawia Ahmed Hassan E.L., Hughes, Peter, Figueres-Esteban, Miguel, Harrison, Chris, Van Gulijk, Coen
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 01-12-2018; Elsevier BV
• Subjects: Big Data; Data management; Data points; Graph database; Indicators; Massive data points; Modelling; Railroads; Railway; Risk; Risk management; Safety; SPAD; Big Data; Risk; Railway; Graph database; SPAD
• ISSN: 0925-7535; 1879-1042
• DOI: 10.1016/j.ssci.2017.11.019

•Explore the benefit of a Big Data modeling approach for safety based on graph databases.•Introduce an approach to building a safety data model integrating multiple data sources.•Demonstrates the applicability of the approach using a case study.•Highlighted that SPAD risk can be understood in new ways.
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.