Assessing Millennial Travel Behaviour and the Implications of Gender: A Case Study of the Greater Toronto and Hamilton Area, Ontario
Bibliographic record
Abstract
In the past decade, as Millennials (those born between 1981 and 2000) reached adulthood they were observed to utilize sustainable modes of transportation more frequently than previous generations. This trend is viewed positively as it results in lower rates of automobility among this generation and is seen as a step towards achieving broader environmental sustainability and traffic reduction objectives. However, prior research has not tested whether these observed sustainable travel behaviour characteristics are manifest equally among males and females within the Millennial generation. Especially, because women face significant travel barriers due to economic, cultural, physical, and/or psychological factors, and their mobility needs differ from those of men. \nThis research examines differences in travel patterns of males and females from the Millennial generation using travel survey data from the Greater Toronto and Hamilton Area (GTHA). Millennials are divided in four age groups: 16-20, 21-25, 26-30, and 31-35. The analysis focuses on variables such as the number of daily trips, driver’s license or transit pass possession, Vehicle Kilometers traveled (VKT), and auto/public-transit trip mode shares. The analysis provides insights into the differences in travel behaviour between male and female Millennials in the GTHA and ways to make the transportation systems in the region safer and more sustainable. \nIn addition to observable differences in automobility trends between Millennials and the preceding generations, the analysis confirms that there is an association between gender and travel behaviour. In the GTHA, women tend to make more daily trips than men, particularly among older Millennials. Additionally, women have substantially lower driver’s license ownership rates than men. Among younger Millennials, women have higher rates of possession of transit passes as well as public transit usage. Further, while full-time employment was found to be associated with higher auto dependency, part-time and being not employed were associated with higher daily trip numbers among older female Millennials in the GTHA. \nThe findings of this study suggest that women have a greater need for flexibility in travel than men, and tend to create multi-modal mobility patterns. The potential increase in automobile dependency among Millennials as they age and the existence of gendered differences in travel behaviour highlight the need for policymakers, including transportation demand management professionals and planners, to take action. This report provides recommendations to respond to these concerns and to ensure that mobility modes are more efficient, safe, reliable, and sustainable. These recommendations include 6 targeted policy recommendations and 3 broad strategies. \nWhile the targeted policy-based recommendations speak in detail about the following: \n1) Enhance transportation mode options for older female Millennials; \n2) Flexible transportation options such as discounted shuttle services for part-time workers; \n3) Improve public transportation infrastructure in suburban areas; \n4) Increase transit pass affordability, especially for part-time females workers; \n5) Gender-inclusive transportation campaigns at the municipality level; \n6) Conduct regular gender impact assessments of transportation plans/programs. \nThe broad strategy-based recommendations involve: \n1) Prioritization of context-specific research and data collection on gender and mobility; \n2) Addressing specific mobility needs of women in the implementation of plans (especially by urban planners and urban designers); \n3) Enactment of women-friendly transportation policies at the local and federal levels.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".