ANALYSIS OF TRAVEL BEHAVIOR OF MILLENNIALS AND OLDER ADULTS
Notice bibliographique
Résumé
The main goal of this thesis is to explore the differences and similarities in travel behavior between millennials/ young adults and older adults. Understanding these differences can help policymakers and transportation providers to better serve the needs of these two generations and to develop strategies to promote greater mobility for all members of a community. To fulfill the study objective, a scoping review of recent publications in developed countries was first conducted to understand the state of research regarding millennials/ young adults and older adults’ travel behavior. Travel behaviors are explored in terms of mode choice, trip distance, trip frequency, use of alternative transport, ridesharing, and mobility tool (i.e., car, bike, transit pass) ownership. Associated factors were categorized into five themes: personal attributes, geography and built environment, living arrangements and family life, technology adoption, and perceptions and attitudes toward travel options and environment. The results of the scoping review indicated that differences exist between generations in terms of travel behavior and that the factors that influence each generation’s travel characteristics are either different or differ in their nature of influence. Next, using cross-sectional data from Hamilton, Ontario, the automobility behavior of millennials/ young adults and older adults were explored. Exploratory analysis of the comparison between young and older adults’ attitudes and preferences towards different travel modes and residential characteristics suggested that the difference between these two groups is marginal in terms of their attitudes toward driving. In general, young and older auto users both showed similar attitudes towards different transportation modes. A similar trend has been seen for non-auto users of young and older adults. Multiple regression analyses were used to explore the automobility behavior of these two generational cohorts. Results suggested that depending on whether a millennial or older adult lives alone, with a partner or in an apartment, their automobility behavior differs. The study also found that positive attitudes and preferences towards sustainable travel behavior make both generations less auto-oriented, especially millennials. Compared to older adults, living arrangements, attitudes and preferences influence millennials’ attributes of automobility behavior to a greater extent. Further, the results suggested that living arrangements, attitudes and preferences can differ among millennials and older adults; therefore, the impact on each of the attributes of automobility behavior will differ. Finally, the study developed a daily travelers’ typology based on attitudes and preferences toward different transportation options. First, the relative probabilities of attitudes and perceptions toward transportation modes are used to define different travel types/groups. Second, the effects of socio-demographics and trip attributes on the likelihood of belonging to these traveler groups are analyzed. Results suggested that heterogeneity exists within travel-related attitudes among different traveler types. Further, heterogeneous traveler types existed among individuals belonging to the same generation, with the same living arrangements, and possession of a driver’s license. Together, the results of the thesis provide an understanding of the diverse transportation needs of millennials and older adults in Hamilton and can lead policymakers and stakeholders toward more effective, equitable and sustainable transportation solutions for both generations.
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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 | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».