Characteristics of cyclist collisions in Ireland: Analysis of a self-reported survey
Notice bibliographique
Résumé
As both a utility mode of transport and recreational activity, cycling has well-known health, environmental, and economic benefits. For these reasons it has been encouraged in many countries, including the Republic of Ireland. However, with increasing popularity there have been concurrent increases in road traffic related cyclist injuries. This study aims to characterise cyclist collisions, which are known to be underreported in Police statistics. For data collection, a survey addressing collisions was distributed to cyclists across the country in 2018. Univariable testing was used to identify differences in collision factors and injury outcomes for cyclist collisions with motorised vehicles, and those where a motorised vehicle is not involved as a collision partner i.e. single cyclist, cyclist-pedestrian, or cyclist-cyclist collisions. Furthermore, binary logistic regression modelling was used to clarify biasing factors for Police reporting of collisions. The largest proportion of collisions was between cyclists and motorised vehicles (56%), followed by single cyclist collisions (29%), collisions with other cyclists (8%), and pedestrians (7%). The odds of Police reporting for collisions with motorised vehicles in this study was 20 times greater than single cyclist collisions, 10 times greater than cyclist-cyclist collisions, and 4 times greater than collisions with pedestrians. The odds of Police reporting of serious injury collisions was 7 times greater than minor injury collisions. There were several differences in road, environmental, and human factors, and injury patterns between cyclist-motorised vehicle collisions and non-motorised vehicle collisions. The findings of this study indicate that greater attention should be paid to the following underreported collision types: 1) those that do not involve collisions with motorised vehicles (single cyclist collisions in particular), which have been shown to have differing collision characteristics to motorised vehicle collisions, and 2) less severe injuries, which have been shown to be a substantial contributor to the cyclist safety problem. Furthermore, surveys have been shown to be a valuable mechanism for investigation of lower severity cyclist injuries, which are largely unrecorded in Police or hospital data.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,006 |
| 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 ».