Crash Involvement of Motor Vehicles in Relationship to the Number and Severity of Traffic Offenses. An Exploratory Analysis of Dutch Traffic Offenses and Crash Data
Notice bibliographique
Résumé
OBJECTIVE: To establish the statistical relationship between offenses and crashes when the unit of analysis is the vehicle instead of the driver, to show the influence of the severity (e.g., minor speed offenses) on this relationship, and to research whether the form of this relationship is similar in different enforcement contexts. METHODS: An exploratory analysis was conducted using Dutch traffic offense and crash data. Crash data included all police-registered crashes involving motorized and registered vehicles in 2009; offense data included all non-criminal traffic offenses registered during 2005-2009 (mostly camera detected). Together these comprise an estimated 97 percent of all traffic offenses registered in this period. The analysis was done on a level of identified vehicles rather than persons. Vehicles involved in crashes were matched to vehicles involved in traffic offenses. The offense frequency distributions of registered crash involved vehicles and a random selection of vehicles was analyzed. Two comparisons were made: (1) privately owned vehicles versus company-owned vehicles and (2) vehicles for which only minor speed offenses were registered versus vehicles for which at least one major speed offense was registered. RESULTS: An increase in traffic offense frequency coincides with a stronger increase in relative crash involvement. This relationship was adequately described by a power function. The slightly more than linear increase in the crash risk for vehicles with only minor speed offenses suggests that minor speed offenses (<10 km/h over the limit) contributed slightly to crashes. This relationship was unlikely to be caused by increased distance traveled only. For vehicles with at least one or more major speed violation an approximately quadratic increase of crash risk with increasing speed offense frequency was found. A comparison of Dutch and Canadian data showed a much more progressive offense-crash relationship in the Dutch data. CONCLUSION: The crash involvement of vehicles increased more than linearly with the number of minor traffic violations. Thus, automatic detection of minor offenses bears relevance to safety. The substantial increase in crash rates with speed offense frequency for vehicles with at least one major speed violation suggests that these vehicles represent a specific group with a significantly increased crash risk, especially in the case of many minor offenses. The more progressive relationship between offenses and crashes in The Netherlands when compared to Canada was hypothesized to result from the higher intensity camera enforcement levels and less severe consequences in the Dutch enforcement and adjudication system.
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 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 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| 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 ».