A big data modeling approach with graph databases for SPAD risk. (December 2018)

• Record Type: Journal Article
• Authors: Rashidy, Rawia Ahmed Hassan E.L.; Hughes, Peter; Figueres-Esteban, Miguel; Harrison, Chris; Van Gulijk, Coen
• Is Part Of: Safety science. Volume 110(2018)Part B
• Page Start: 75
• Page End: 79
• Publication Date: 2018-12
• Subjects: Big Data -- Railway -- Risk -- SPAD -- Graph database; Industrial accidents -- Periodicals; Accident Prevention -- Periodicals; Safety -- Periodicals; Travail -- Accidents -- Périodiques; 363.11
• DOI: 10.1016/j.ssci.2017.11.019
• Languages: English
• ISSNs: 0925-7535

Abstract

Highlights: 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. 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.