Notice bibliographique
Résumé
Introduction 1132 I. Analyzing Transit and Jobs for Sustainability 1135 A. The Importance to Society and Sustainability of Transit and its Proximity to Jobs 1135 B. Methodologies for Transit and Job Relationships 1136 C. General Patterns and Trends Nationwide for Selected Bus and Rail Transit Systems 1137 II. Heavy Rail and Other Fixed Guideway Modes 1138 A. Shares of Transit Modes Relative to Other Modes 1138 B. U.S. Rates of Change in Heavy and Light Rail Transit 1140 III. HR Systems and Job Characteristics Around HR Stations in New York City 1141 A. Job Density Change 1142 B. Job Density or Concentration 1144 IV. Connectivity and Support Among Transit Systems 1146 V. Factors Influencing the TOD/Job Phenomenon 1147 Conclusion 1151 INTRODUCTION Transit has supported sustainability within, around, and connected to urban areas through job attraction, environmental and climate benefits, and other job-related benefits associated with proximity to transit. The extent to which jobs are attracted to transit varies by urban area, type of rail system, and employment sector. (1) The relationship between jobs and transit accessibility is an important component of sustainability. Development around transit is typically referred to as transit-oriented development (TOD). The U.S. Environmental Protection Agency (EPA) has defined the concept as a compact development built around a transit station or within easy walking distance (typically a half-mile) of a station and containing a mix of land uses such as housing, offices, shops, restaurants, and entertainment. (2) The type of development varies. The concept of the TOD is an old one, and a review by Ian Carlton linked it directly to sustainability and identified its introduction initially with Peter Calthorpe and the expansion of the term by others. (3) Other reviews similarly support the relationship between rail transit and job density. (4) Transit systems are typically categorized as including light rail transit (LRT), bus rapid transit (BRT), street car transit (SCT), commuter rail (CR), and heavy rail (HR). (5) In his article, Professor Nelson evaluates LRT, SCT, and BRT with respect to job attraction, transit accessibility, and characteristics of urban areas that support the job and transit relationship for groupings of certain economic sectors within defined alternative radii around transit. (6) Studies other than Nelson's have examined this relationship using fewer nodes and geographic areas. (7) Nelson's study included eleven LRT, three SCT, and eight BRT systems. (8) These account for about half of the LRT systems, about a third of the SCT systems, and all of the BRT systems in the U.S. (9) The LRT, SCT, and BRT transit systems serve more decentralized populations relative to those served by HR; LRT, SCT, and BRT have been important drivers of TODs, as Nelson observes, and their growth rates support Nelson's focus on these systems. (10) LRT in particular has grown substantially in terms of the number of systems, passenger trips, and miles traveled, as indicated by the analyses of data from the American Public Transportation Association (APTA) and the U.S. DOT National Transit Database (NTD). (11) While LRT, SCT, and BRT modes generally account for a lower share of transit ridership, they have been shown to have grown the fastest during many time periods according to the APTA and NTD. (12) This Article complements Nelson's emphasis on LRT, SCT, and BRT in highlighting how HR transit also supports job growth. Given the higher ridership that HR can and does support, it has the potential to attract jobs on a larger scale. …
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,002 | 0,005 |
| Communication savante | 0,005 | 0,009 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 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 source (Gemma direct ou Codex distillé), 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 ».