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Big Data for Risk Analysis: the future of safe railways

Figueres-Esteban, Miguel, Hughes, Peter and Van Gulijk, Coen (2016) Big Data for Risk Analysis: the future of safe railways. In: XII Conference on Transport Engineering, 7th - 9th June 2016, Valencia, Spain. (Unpublished)

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Abstract

New technology brings ever more data to support decision-making for intelligent transport systems. Big Data is no longer a futuristic challenge, it is happening right now: modern railway systems have countless sources of data providing a massive quantity of diverse information on every aspect of operations such as train position and speed, brake applications, passenger numbers, status of the signaling system or reported incidents.
The traditional approaches to safety management on the railways have relied on static data sources to populate traditional safety tools such as bow-tie models and fault trees. The Big Data Risk Analysis (BDRA) program for Railways at the University of Huddersfield is investigating how the many Big Data sources from the railway can be combined in a meaningful way to provide a better understanding about the GB railway systems and the environment within which they operate.
Moving to BDRA is not simply a matter of scaling-up existing analysis techniques. BDRA has to coordinate and combine a wide range of sources with different types of data and accuracy, and that is not straight-forward. BDRA is structured around three components: data, ontology and visualisation. Each of these components is critical to support the overall framework. This paper describes how these three components are used to get safety knowledge from two data sources by means of ontologies from text documents. This is a part of the ongoing BDRA research that is looking at integrating many large and varied data sources to support railway safety and decision-makers.

Item Type: Conference or Workshop Item (Paper)
Subjects: T Technology > TF Railroad engineering and operation
Schools: School of Computing and Engineering
School of Computing and Engineering > Institute of Railway Research
Related URLs:
Depositing User: Miguel Figueres Esteban
Date Deposited: 16 May 2016 10:36
Last Modified: 02 Dec 2016 05:20
URI: http://eprints.hud.ac.uk/id/eprint/28361

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