Association Between Public Transit Use and Happiness Across Four Canadian Cities: A Multilevel Analysis Using Longitudinal Data
Notice bibliographique
Résumé
Introduction: Our daily travel habits can affect how we feel. For cities to be resilient and habitable, sustainable transportation is essential. As cities are increasingly interested in well-being, better understanding the impact of people’s use of public transit can help design happier cities. This research explores how transit use is linked to happiness, specifically accounting for gender and educational attainment. Objectives: The objective of my research is to examine the relationship between residents’ use of public transit and happiness in four Canadian cities (Victoria, Vancouver, Montréal, and Saskatoon). I hope to contribute to the literature examining sustainable transportation by bringing attention to the possible association between public transit and happiness. Research Questions: I investigated three key questions: 1. What is the association between public transit use and happiness in four Canadian cities? 2. Do gender and educational attainment modify or interact with the association between public transit use and happiness in four Canadian cities? 3. Do gender and educational attainment mediate the relationship between public transit use and happiness? Methods: I used data collected from four Canadian cities (Victoria, Vancouver, Montréal, and Saskatoon) over two waves between May 2017 and February 2021 from 3,539 participants of the INTERACT (The INTerventions, Equity, Research, and Action in Cities Team) study. INTERACT is a population health urban intervention research program. The outcome variable, happiness, was measured using the Subjective Happiness Scale (SHS), which captures individuals’ overall self-evaluation of happiness and reflects the hedonic dimension of happiness—emphasizing pleasure, life satisfaction, and positive emotional states. I conducted multilevel linear models (MLMs) regression to examine the association between happiness and self-reported use of public transit. In separate models, I examined the association between frequency of public transit use, among public transit users, and happiness. I further tested the potential effect-modifying and mediating roles of gender and educational attainment in the association between public transit use and happiness. Results: Descriptive analyses revealed that most participants reported moderate to high happiness levels, with notable differences by gender, education, and city. In multilevel models including all transportation users, transit use was not significantly associated with happiness overall. Among transit users only, more frequent transit use was associated with lower happiness, although this effect was attenuated after adjusting for covariates. Mediation analyses showed that education significantly mediated the transit–happiness relationship, while gender had a weaker, marginal effect. Adjusted models consistently demonstrated better fit and more normally distributed residuals, supporting the robustness of the findings. Conclusion: Knowing the relationship between public transit and happiness can help guide decisions regarding policy and urban development strategies. This research aims to support happy and sustainable communities by improving transit systems and making them more appealing and efficient. Urban areas can improve their overall livability and quality of life by putting their citizens’ happiness first when designing transportation systems.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,011 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».