Why Not Bike? : Exploring the Cycling Behaviours of UBC Cyclists
Notice bibliographique
Résumé
The University of British Columbia (UBC) sees over 76,000 trips to and from its campus on a daily basis. These trips occur through many means of transportation, ranging from personal vehicles, public transportation, cycling, and walking, to name a few. Despite the city of Vancouver and UBC’s efforts to improve infrastructure and interest, minimal improvements have been made regarding the percentage of those who cycle to campus. Within this group is an even smaller community of those who cycle regularly (2-5 times a week), leading most to cycle infrequently, or irregularly (less than twice a week). Past research has identified barriers such as perceived safety, confidence, and experience of the cyclists being reason to their infrequency (Kelarestaghi, Ermagun, & Heaslip, 2019). The goal of this study is to identify the cycling behaviours of UBC commuter cyclists and to determine the barriers that prevent more commuters, specifically irregular cyclists, from increasing their cycling frequency. We also aim to provide insight to UBC and other relevant organizations, governments, and stakeholders involved in the cycling community to improve cycling infrastructure and promotion. The online platform ‘Qualtrics’ was utilized to create an online survey consisting of multiple choice and fill-in-the-blank questions to allow a wide distribution to many UBC students, faculty, and staff. The questions included in the survey evaluated sociodemographic factors of the respondents as well as more specific questions regarding their cycling habits and improvements they would like to see regarding cycling infrastructure and promotion. The incentive of a gift card prize draw was provided to increase interest. In total we received 88 responses with only one response from a non-student, which led us to discard that singular response to improve consistency. Correlation between opinions and age/gender were found, as well as consistency among cyclists and non-cyclists regarding their views on factors such as barriers and required improvements. The largest barriers identified by the group were not having a functional bicycle, distance from their residence to campus, and adverse weather the city of Vancouver faces. The majority of respondents had no experience cycling to campus. Lack of bicycle ownership was an issue in this instance. Within the group that did cycle to campus, barriers to cycle more frequently included personal safety such as feelings of safety although no further detail was provided. Major limitations of the study include sampling bias and providing more in depth questions to explain behaviour. The study was not distributed widely enough to receive responses from individuals from a variety of different sectors of UBC meaning that our sample is not representative of the population. Furthermore, although our questions were descriptive they did not provide enough detail to explain behaviour of respondents such as why males felt more unsafe cycling than females. Our recommendations include providing education to cyclists to improve their confidence and feelings of safety and providing monetary incentives to increase interest in cycling. Regarding our research process, adjusting our survey to provide more specific questions to irregular cyclists to explain their behaviours and accessing a more representative population of UBC would be recommendations. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”
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 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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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.
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 ».