Estimating the frequency of trains approaching red signals: A case study for improving the understanding of SPAD risk

Yunshi Zhao, Julian Stow, Chris Harrison

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

This study describes a novel technique for estimating the frequency with which trains approach signals showing a red aspect. This knowledge is potentially important for understanding the likelihood of a signal being passed at danger (SPAD) at individual signals and also for normalisation of SPAD data, both locally and nationally, for trending and benchmarking. The industry currently uses estimates for the number of red aspect approaches based on driver surveys which are considered to have significant shortcomings. Data for this analysis is sourced from publicly available live feeds provided by Network Rail which give information on train movements and signal states. The development of the analysis model and supporting software are described and some sample results from case studies are presented. An initial study of seven signalling areas showed that approximately 3.3% of all signal approaches are to red signals. However, it also highlighted that there is a large variation in the red approach rates between signalling areas and between individual signals. SPAD risk assessment at individual signals may be significantly enhanced by the ability to estimate red approach rates for individual signals using the techniques described.

LanguageEnglish
Pages579-586
Number of pages8
JournalIET Intelligent Transport Systems
Volume10
Issue number9
Early online date27 Oct 2016
DOIs
Publication statusPublished - Nov 2016

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train
Benchmarking
Risk assessment
Rails
model analysis
benchmarking
normalization
risk assessment
Industry
driver
industry
ability
software

Cite this

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Estimating the frequency of trains approaching red signals : A case study for improving the understanding of SPAD risk. / Zhao, Yunshi; Stow, Julian; Harrison, Chris.

In: IET Intelligent Transport Systems, Vol. 10, No. 9, 11.2016, p. 579-586.

Research output: Contribution to journalArticle

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