Phase 1: Evaluation of leading and lagging performance indicators
Notice bibliographique
Résumé
Canada has seen an increase in the amount of Dangerous Goods (DG) transported by rail by approximately 25% since 2004. Furthermore, Transport of Dangerous Goods (TDG) is forecasted to continue increasing. Sustainable growth in TDG by rail will require management of risk to acceptable safety levels. The first part of this study focuses on key occurrence types for TDG, incident causes, risk control strategies, and analysis of leading and lagging safety indicators. We also reviewed current safety performance and Canadian railway incident databases. Our results suggested that the performance against lagging indicators currently being reported is adequate, including derailments and collisions (main and non-main track), serious injuries (including fatalities), DG leakers, and releases. Also, a list of rail accidents with the greatest number of fatalities was used to calculate a crude estimate of societal risk associated with rail transportation. According to UK Health and Safety Executive recommendations, estimated rail transport risks can be considered acceptable when assessed at a milepost scale. However, there are opportunities for further enhancing safety reporting, management, and performance. One potential area of improvement exists in the field of Safety Management Systems (SMS). The second task of this study focuses on enhancing railway SMS, particularly areas of SMS that might directly contribute to reducing the number of DG main-track train derailments. We applied detailed Root Cause Analyses (RCA), Bow Tie Analysis (BTA), and incident database analysis to identify the main causes and consequences of these types of accidents (2007-2017). The relationship between these factors and gaps in SMS elements were then identified and the frequency of each factor investigated. The results showed that the main gaps are related to process and equipment integrity, incident investigation, and company standards, codes, and regulations. We present some recommendations to improve the management of each SMS element and reduce these gaps. In the last part of this study, we applied the Human Factors Analysis and Classification System (HFACS) approach to analyze 42 main-track derailments and collisions from 2007 to 2018 with the intention of studying the effect of human factors. Associations between adjacent sub-categories of the HFACS framework were analyzed to identify interdependencies between active and latent errors using chi-square tests and Kruskal’s lambda analysis. Furthermore, we implemented Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Analytical Network Process (ANP) methods to identify causal relationships between different sub-categories and calculated the weighted influence of each sub-category on main-track derailments and collisions. Finally, we compared this work and other studies that found relationships between active and latent errors in the railway industry. We found good agreement between the results of these studies, which highlights the importance of supervisory and organizational factors in the prevention of railway loss incidents. Based on these findings, we make several recommendations to reduce railway loss incidents.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,036 | 0,062 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,006 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,002 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».