A Method for Classifying Red Signal Approaches Using Train Operational Data
The paper describes a novel technique to reliably classify whether or not a train approaching a signal at red actually came to a stand before the signal cleared (red aspect approaches) using the data from conventional signaling systems. This data is limited to the times at which the signal aspect changes and the times at which the train enters and leaves the signaling section (‘berth’) in advance of the signal. Knowing the percentage of red aspect approaches is potentially important for understanding the likelihood of a signal being passed at danger (SPAD) at individual signals and also for normalization of SPAD data, both locally and nationally, for trending and benchmarking. The industry currently uses the number of red aspect approaches based on driver surveys as estimates, which are considered to have significant shortcomings. The development of the classification model is described together with the validation procedures. The techniques presented in this paper allow red approach rates to be reliably determined without the need for complex integration of signaling system and on-train data recorder data. The initial study of 94 million train approaches shows that 5% of them stopped at red aspects. It is also highlighted that there is a large variation in the red aspect approach rates between signaling areas and between individual signals. SPAD risk assessment at individual signals could be significantly enhanced by the ability to estimate red aspect approach rates using the techniques described.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/09257535
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Supplemental Notes:
- © 2017 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
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Authors:
- Zhao, Yunshi
- Stow, Julian
- 0000-0002-3031-804X
- Harrison, Chris
- Publication Date: 2018-12
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 67-74
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Serial:
- Safety Science
- Volume: 110
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 0925-7535
- Serial URL: http://www.sciencedirect.com/science/journal/09257535
Subject/Index Terms
- TRT Terms: Classification; Data collection; Railroad safety; Railroad signals; Risk assessment; Signal lights; Train operation
- Identifier Terms: Signal Passed at Danger
- Subject Areas: Data and Information Technology; Operations and Traffic Management; Railroads; Safety and Human Factors;
Filing Info
- Accession Number: 01684485
- Record Type: Publication
- Files: TRIS
- Created Date: Oct 29 2018 9:34AM