Lessons from the Green Lanes: Evaluating Protected Bike Lanes in the U.S.
Notice bibliographique
Résumé
This report presents finding from research evaluating U.S. protected bicycle lanes (cycle tracks) in terms of their use, perception, benefits, and impacts. This research examines protected bicycle lanes in five cities: Austin, TX; Chicago, IL; Portland, OR; San Francisco, CA; and Washington, D.C., using video, surveys of intercepted bicyclists and nearby residents, and count data. A total of 168 hours were analyzed in this report where 16,393 bicyclists and 19,724 turning and merging vehicles were observed. These data were analyzed to assess actual behavior of bicyclists and motor vehicle drivers to determine how well each user type understands the design of the facility and to identify potential conflicts between bicyclists, motor vehicles and pedestrians. City count data from before and after installation, along with counts from video observation, were used to analyze change in ridership. A resident survey (n=2,283 or 23% of those who received the survey in the mail) provided the perspective of people who live, drive, and walk near the new lanes, as well as residents who bike on the new lanes. A bicyclist intercept survey (n= 1,111; or 33% of those invited to participate) focused more on people’s experiences riding in the protected lanes. A measured increase was observed in ridership on all facilities after the installation of the protected cycling facilities, ranging from +21% to +171%. Survey data indicates that 10% of current riders switched from other modes, and 24% shifted from other bicycle routes. Over a quarter of riders indicated they are riding more in general because of the protected bike lanes. A large majority of drivers and bicyclists stated that they understood the intent of the intersection designs and were observed to use them as intended, though specific designs perform better than others on certain tasks. No collisions or near-collisions were observed over 144 hours of video review for safety at intersections, including 12,900 bicyclists. Residents and bicyclists indicated that any type of buffer shows a considerable increase in self-reported comfort levels over a striped bike lane, though designs with more physical separation had the highest scores. Buffers with vertical physical objects (those that would be considered protected lanes - e.g. with flexposts, planters, curbs, or parked cars) all resulted in considerably higher comfort levels than buffers created only with paint. Flexpost buffers got very high ratings even though they provide little actual physical protection from vehicle intrusions— cyclists perceive them as an effective
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) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».