Hotspots for Vessel-to-Vessel and Vessel-to-Fix Object Accidents Along the Great Lakes Seaway
Notice bibliographique
Résumé
This research focuses on the freight vessel accidents occurring on the Great Lakes Seaway (GLS) extending from Rimouski (on the St. Lawrence) to Sault Ste. Marie (connecting Lake Huron to Lake Superior). Over the past decade, an average of 112 vessel accidents per year have been reported along the GLS, 20% of which took place as a result of groundings and collisions with other vessels. The vast majority of these accidents took place on river and canal/lock segments of the seaway. Freight vessel accidents along the GLS tend to be clustered at specific unsafe locations. In this research, locations with high vessel accident occurrence are referred to as hotspots. The first step in vessel accident reduction is to identify hotspots along the GLS, which then become prime candidates for future safety intervention initiatives. Given the rare and random nature of vessel accidents, the identification of hotspots needs to be based on robust site-specific prediction models. This research presents an empirical Bayes prediction model developed for the Great Lakes Seaway (GLS) that considers four types of accident scenarios: vessel-to-vessel (VV) and vessel-to-fix objects (VF) for river and canal/lock sections. Hotspot sites are determined using two risk tolerance thresholds: 95th percentile exceedance (high risk sites) and 85th percentile exceedance (moderate and high-risk sites). For the 95th percentile threshold and VV accidents, a total of five hotspots were identified over the 1600 km length of the GLS being studied (excluding lake or port areas). Of the designated hotspot sections, 10 km (60.6% of the total hotspot length) were located along natural river courses and the rest at canals/locks. For VF accidents, all of the high-risk hotspots were located at canal/lock sections (a total of 15.5 km). Reducing the threshold to 85th percentile resulted in a 7.8% increase in seaway length that is designated as a hotspot. VV and VF accidents were combined and for these accidents, hotspots were obtained for the 95th and 85th percentile thresholds. For the 95th percentile a total of five sections (14 km, 0.88% of the seaway) were identified as hotspots, and for the 85th percentile the number of hotspots were increased to 16 sections (47.72 km, 3% of the seaway). These unsafe locations were also compared with observed historical accidents along the GLS, and the location of the observed accidents were found to be consistent with hotspot designated sections along the GLS for both thresholds.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».