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Enregistrement W4362647343 · doi:10.1109/rams51473.2023.10088188

Recommendations to Improve Quality of Safety Indicators in the Railway Industry

2023· article· en· W4362647343 sur OpenAlexaffabout
Behrooz Ebrahimi, Nicole L. Henderson

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineHealth Professions
ThématiqueOccupational Health and Safety Research
Établissements canadiensSNC-Lavalin (Canada)
Organismes subventionnairesnon disponible
Mots-clésRisk analysis (engineering)Warning systemTransport engineeringQuality (philosophy)System safetyTrack (disk drive)Safety caseStock (firearms)Computer scienceBusinessEngineeringReliability engineeringTelecommunications

Résumé

récupéré en direct d'OpenAlex

Summary & ConclusionsDespite the overwhelming number of currently available safety management systems, accidents still periodically occur in the railway industry. This paper analyses recent incidents and accidents in the Canadian railway industry, showing that in many of these cases, identifiable precursors were present in the trajectory of these accidents. Several repeating precursors can be identified from the railway accident reports in the Transportation Safety Board of Canada (TSB) database. Examples include damage to non-critical safety equipment, operator complaints, maintenance problems, quality problems, or, more importantly, similar past incidents for which either the underlying causes were not correctly identified, or the mitigation measures were not satisfactorily implemented.These recurring precursors and patterns of precursors can be seen as warning signals. Their existence indicates a gap between the actual status of a system with regard to safety and the common proactive safety indicators. This gap consists of information, already present and available in the industry in some form, but unavailable to the system safety analyst; it results in situations where safety indicators, such as risk analyses, do not accurately represent the system under consideration. This paper argues that these reoccurring precursors, were they included in the safety indicators, would provide a clearer picture of the actual safety deficiencies of the systems and aid in the prevention of similar accidents.This paper will primarily focus on analyzing the data from "unplanned/uncontrolled movement of rolling stock" occurrences from main track or sidings, in order to find opportunities for further enhancing safety reporting, management, and performance. Uncontrolled movements are relatively rare events, which, despite their low probability of occurrence, can have catastrophic consequences—particularly if the rolling stock involved are carrying dangerous goods and are unattended. The Lac-Mégantic rail accident of 2013 demonstrated that the cost to human life and our communities can be incalculable.Despite significant safety action taken by Transport Canada and the railway industry since the Lac-Mégantic accident to reduce the probability of unplanned/uncontrolled movements of rail equipment, this type of occurrence has continued to trend upwards, posing a significant risk to the rail transportation system. The increase in these occurrences is particularly important in light of the fact that the amount of dangerous goods transported by rail within Canada has increased by an average of approximately 25% since 2004, with a 42.5% increase in transported fuels and chemicals between 2011 and 2017. Further, movement of dangerous goods by rail is forecasted to continue increasing. Sustainable growth in the transport of dangerous goods by rail will require acceptable safety levels.It is argued in this paper that one of the main contributors to the problem of repeated similar incidents and accidents originates from the fragmentation of the railway industry. The rail industry now operates as a complex web of different operating companies, infrastructure management companies, regulatory bodies, and contractors. Even though this fragmentation is understandable and even beneficial in supporting the wide range of freight and light rail applications internationally, inconsistent reporting of accidents and incidents impedes the improvement of safety, industry wide.We believe that a more centralized and integrated incident reporting system similar to those in industries like Nuclear or Aviation could improve the understanding of potential risks, the design of new systems, and guide regulation. The data from such reports, available in a publicly accessible database, could be used for validating safety analyses, improving quantitative analyses, and to lower precursor frequency through informed design. As a result, the probability of more serious accidents may be reduced, and the safety of daily operation of railway systems improved.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,081
score de la tête « metaresearch » (Gemma)0,207
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,098
Score d'incertitude au seuil0,428

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0810,207
Méta-épidémiologie (sens strict)0,0030,001
Méta-épidémiologie (sens large)0,0020,003
Bibliométrie0,0100,010
Études des sciences et des technologies0,0030,002
Communication savante0,0140,012
Science ouverte0,0080,005
Intégrité de la recherche0,0080,008
Charge utile insuffisante (le modèle a refusé de juger)0,0240,010

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,207
Tête enseignante GPT0,563
Écart entre enseignants0,355 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2023
Routes d'admission2
Résumé présentoui

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