Opportunities for Wearable Technology to Increase the Safety of Rail Sector Workers
Notice bibliographique
Résumé
Transport Canada’s Innovation Centre supports emerging transportation technologies to help ensure Canadians can benefit from a safe, secure, clean, and integrated transportation system. From the standpoint of safety in rail transportation, the Centre is interested investigating the viability of using wearable technologies to increase the safety of rail sector workers. Although wearable technologies have proven to be useful in other industries, their adoption in Canadian rail has yet to gain traction. This study aims to show that wearable technologies have the potential to increase the safety of rail sector workers and that further investigation of specific use cases could be valuable.To achieve the objectives of the study, FactorSafe Solutions, an Ottawa, Ontario based human factors consultancy, was contracted to collect and analyse relevant data from multiple sources and then to report on their findings. The data collection methods were three-fold: a literature and market review of known human factors considerations of trackside and yard workers and existing technologies that may be suited to address those considerations; an analysis of the past five years of reported rail occurrences found on the Transportation Safety Board’s Rail Occurrence Database System to determine the most common types of occurrences where wearable technologies may have mitigated the risk levels; and a series of interviews with subject matter experts from the rail industry as well as researchers in the field of rail safety and associated technologies to validate the previous findings as well as uncover new information.By synthesizing the analysed data from the three data collection tasks, it was concluded that there are 11 relevant occurrence types, the highest priority of which include non-main track derailments, non-main track collisions, and movements exceed limits of authority, for which yard and trackside workers could potentially benefit from the implementation of specific wearable technologies. The 11 occurrence types are spread across both the yard and tracksideenvironments and could potentially be addressed through a variety of different wearable technologies. An important conclusion of the study is that there is not likely to be a single solution to meet the needs of all workers, environments, or tasks.Finally, a research framework is proposed to guide Transport Canada’s Innovation Centre through the potential next steps. The framework includes foundational research to build on the knowledge of the prioritized use cases and technologies, pilot studies conducted in nonoperational simulated settings with small groups of participants, and then larger field trials to assess performance of the wearable technologies during actual operations. A key successfactor to the research framework is to engage with the rail industry to benefit from their knowledge and resources, including incorporating their safety management systems with a human factors risk assessment during pilot studies to ensure that the wearable technologies do not introduce new safety risks.
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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 | 0,005 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
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 ».