ANALYSIS OF TRAVEL BEHAVIOR OF MILLENNIALS AND OLDER ADULTS
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".