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Enregistrement W2109146397

Accessibility- vs. Mobility-Enhancing Strategies for Addressing Automobile Dependence in the U.S

2002· article· en· W2109146397 sur OpenAlexaboutno aff
Susan Handy

Notice bibliographique

RevueeScholarship (California Digital Library) · 2002
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueUrban Transport and Accessibility
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésKilometerPopulationAgricultural economicsGeographyGovernment (linguistics)DemographyEconomicsEngineeringTransport engineering
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Prepared for the European Conference of Ministers of TransportIn 1995, the average American spent 56 minutes a day in a car, a 14 percent increase from only five years earlier. The average American household drove over 33,000 kilometers per year, and the average American car was driven over 19,000 kilometers per year. The growth in total vehicle-kilometers-traveled in the U.S. has continued unabated for decades, far exceeding the growth in population. The U.S. is clearly the most auto-dependent society on earth, but other parts of the world are catching up. By 2000, there were more cars per person in Germany than in the U.S., and nearly as many in Sweden, France, and Canada. The average vehicle in the United Kingdom was driven over 17,000 kilometers per year, just 11 percent behind the average in the U.S. Automobile dependence is growing throughout the world.Growth in automobile travel has been well supported by public investments in roads. Total capital outlays for roads in the U.S. by all levels of government have totaled between $30 billion and $50 billion per year (in constant 2000 dollars) for decades and have approached $60 billion per year in recent years. By 2000, the U.S. had over 3.9 million miles of roads, including over 21,000 miles of freeways in urban areas, and the annual cost of maintaining this system had reached nearly $30 billion per year. From the beginning, the mission of the U.S. Department of Transportation has been to accommodate the growing demand for vehicle travel. Today, the department has established "mobility" as one of its strategic goals and uses trends in vehicle travel as an indicator of progress towards this goal. In its 2001 "Report to the American People," the Federal Highway Administration said, "we must continue to invest in America's highways in order to achieve our national goals".But the investments in roads have not kept up with the growth in vehicle travel. Between 1941 and 2000, total kilometers of roads in the U.S. increased by 145% but vehicle-kilometers-traveled increased by 724%. The gap is significant even when accounting for population growth: kilometers of roads per person increased by 16% while vehicle-kilometers-traveled per person increased by 290% between 1941 and 2000. As demand has outpaced supply, levels of congestion have increased. The Texas Transportation Institute (TTI) calculates that in the 68 largest metropolitan areas in the U.S. the average annual hours of delay per person grew from 11 in 1982 to 36 in 1999, an increase of 227%. The estimated cost of this delay reached $77.8 billion in 1999. How much more road building would it take to eliminate this delay? TTI estimates that metropolitan areas added only 48% of the roads they needed to keep up with the growth in vehicle travel in 1999.At the same time, the environmental consequences of this steady growth in automobile use are well known. Although air quality is better now in places like Los Angeles than it has been in decades, the problem is far from solved. In the U.S., emissions of volatile organic compounds from transportation have been decreasing steadily for the last three decades, but emissions of nitrogen oxides have been going up, and 36 areas that are home to a total of 85 million people still fail to officially meet the national standards for ozone. The transportation sector dumped 513 million metric tons of carbon dioxide, a major greenhouse gas, into the atmosphere in 2000, a 3.43% increase from the year before. In 1999, the U.S. consumed 19.5 million barrels of oil per day, 26.5% of the world's consumption; 68% of oil consumption in the U.S. was for transportation, and consumption of oil in the U.S. for transportation alone exceeded total production of oil in the U.S. by 50%. These statistics and others seem to provide ample justification for policies to reduce automobile use.That leads to something of a dilemma for policy makers. Should policies focus on accommodating growing levels of vehicle travel because driving more is apparently what the public wants to do? Or should policies focus on limiting driving so as to reduce environmental and other costs? The former strategy has so far been more politically palatable, at least in the U.S., but it is also becoming increasingly unaffordable. The latter strategy means reversing a trend that has slowed only for wars and recessions and goes against American traditions of freedom of movement. So what's the right thing to do? One obvious approach is to push for further improvements in vehicle and fuel technologies that will reduce the environmental impacts of driving without in anyway limiting driving. But that leaves the problem that driving is growing faster than capacity possibly can. It also leaves the problem that a significant share of the population cannot drive or does not have access to a car, for reasons of income, age, or ability. An alternative approach that is gaining wide support in the U.S. is to reduce the need for driving by bringing activities closer to home, by improving the quality of transit, bicycling, and walking – by enhancing accessibility. Such an approach represents a fundamental shift from a traditional focus on enhancing mobility through road building. This report looks at what it means to focus on enhancing accessibility rather than enhancing mobility, first by defining these concepts then by reviewing the U.S. experience with mobility-enhancing strategies, accessibility-enhancing strategies, and others.

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)0,002
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: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,024
Score d'incertitude au seuil0,048

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,001
Communication savante0,0020,002
Science ouverte0,0010,003
Intégrité de la recherche0,0020,001
Charge utile insuffisante (le modèle a refusé de juger)0,0090,001

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.

Tête enseignante Opus0,045
Tête enseignante GPT0,291
Écart entre enseignants0,246 · 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

Citations66
Publié2002
Routes d'admission1
Résumé présentoui

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