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Enregistrement W2266265215

“Trucks and SUVs in the Twin Cities Metropolitan Area: How Dangerous Are They?”

2012· article· en· W2266265215 sur OpenAlexaboutno aff
Jinhyung Lee, Pinar Karaca‐Mandic

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineEngineering
ThématiqueTraffic and Road Safety
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésTruckMetropolitan areaTransport engineeringLiabilityBusinessEnvironmental healthLight railOperations managementMedical emergencyMedicineEngineeringFinanceAutomotive engineeringPublic transport
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Light or heavy trucks and sport-utility vehicles (SUVs) are increasingly popular, in part because they provide improved protection to their own passengers in an accident. However, when an accident occurs, these vehicles also cause greater injury to passengers of other vehicles, pedestrians, bicyclists, and motorcyclists. Because the costs of these injuries are external costs in no-fault auto-liability systems such as those used in Minnesota, consumers may have inefficiently high incentives to purchase light/heavy trucks and SUVs. This study investigated the extent of injury costs associated with light/ heavy trucks and SUVs in the Twin Cities metropolitan area. We estimated the relationship between the type of vehicle in accidents involving one light/heavy truck or SUV and one standard passenger car and the level of vehicle damage caused, the likelihood of hospital admissions resulting from the accident, and the hospital charges for those hospitalized from the accident. The analysis showed that the likelihood of hospital admission was highest for occupants of standard cars and lowest for occupants of light/heavy trucks or SUVs. For accidents involving hospitalization, occupants of standard cars also incurred higher hospitalization charges on average compared with occupants of light/heavy trucks or SUVs. These findings suggest that light/heavy trucks and SUVs benefit their occupants in terms of the likelihood of a hospital admission and hospital charges stemming from an accident, but do so at the expense of standard-car occupants. We suggest several policy changes that would internalize the costs that light/heavy trucks and SUVs impose on occupants of other vehicles and pedestrians, and would lead to a more optimal mix of vehicle types in the nation’s vehicle fleet. The research upon which this article is based was supported by a grant from CURA’s Faculty Interactive Research Program. Motor-vehicle accidents are the leading cause of injury-related death for all age groups in Minnesota and the nation. According to the Minnesota Department of Health’s Injury and Violence Prevention Unit, 50% of serious traumatic brain injuries and 60% of spinal cord injuries in Minnesota result from a motor-vehicle accident. Among motor-vehicle accident victims, pedestrians, young people (15–24 year olds), the elderly, and male drivers, as well as unbelted passengers and unrestrained children, are at higher risk of injury or death.1 Minnesota has a “no-fault” autoliability system which, in contrast to the more common “tort system,” limits a driver’s liability for their actions. Evidence is mixed about whether or not no-fault liability systems alter driving behavior and reduce incentives to drive carefully.2 1 Minnesota Department of Health, Injury and Violence Prevention Unit, Ten Leading Causes of Nonfatal Hospitalized Injury by Age Group and Gender, Minnesota (St. Paul: Minnesota Department of Health, 2001), www.health.state.mn.us/injury/pub /ed2001/ed01cause23.pdf. 2 Several studies found little or no evidence that accidents rates are different in no-fault states—see P.S. Kochanowski and M.V. Young, “Deterrent Aspects of No-Fault Automobile Insurance: Some Empirical Findings,” Journal of Risk and Insurance 52,2 (1985): 269–288; D.S. Loughran, The Effect of No-Fault Automobile Insurance on Driver Behavior and Automobile Accidents in the United States (Santa Monica, CA: RAND Corporation, 2001); R.A. Derrig This concern is particularly important given the change in the composition of motor vehicles on the roads during the last few decades. According to the National Highway Traffic Safety Administration, the proportion of light or heavy trucks and sport-utility vehicles (SUVs) has increased from 22% of motor vehicles on the road in 1980 to 39% in 2000. Research attributes their popularity to the fact that they provide improved protection to et al., “The Effect of Population Safety Belt Usage Rates on Motor Vehicle-Related Fatalities,” Accident Analysis and Prevention 34,1 (2002): 101–110; P. Heaton and E. Helland, “No-Fault Insurance and Automobile Accidents,” unpublished paper, April 2010, works.bepress.com/psheaton/7/. Other studies point to empirical evidence that no-fault laws lead to significant increases in traffic fatalities—see E.M. Landes, “Insurance, Liability, and Accidents: A Theoretical and Empirical Investigation of the Effect of No-Fault Accidents,” Journal of Law and Economics 25,1 (1982): 49–65; R.A. Devlin, “Liability versus No-Fault Automobile Insurance Regimes: An Analysis of the Experience in Quebec,” in G. Dionne (ed.), Contributions to Insurance Economics (Norwell, MA: Kluwer Academic Publishers, 2010), pp. 499–520; J.D. Cummins et al., “The Incentive Effects of No-Fault Automobile Insurance,” Journal of Law and Economics 44 (2001): 427–464; and A. Cohen and R. Dehejia, “The Effect of Automobile Insurance and Accident Liability Laws on Traffic Fatalities,” Journal of Law and Economics 47 (2004): 357–359. © Frnces Tw ittyStockp hoo .com

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,238
Score d'incertitude au seuil0,474

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0000,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,016
Tête enseignante GPT0,199
Écart entre enseignants0,184 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2012
Routes d'admission1
Résumé présentoui

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