Understanding Travel Behaviours and Overcoming Barriers: Case Studies of Achieving Equitable Public Transit for Disadvantaged Groups in Canada
Bibliographic record
Abstract
Having access to transportation is essential for everyone to participate in daily activities, such as employment, healthcare, education, or social interaction. Socially disadvantaged groups, including low-income earners, single parents, immigrants, and people with disabilities often encounter travel barriers that prevent them from participating in activities, negatively impacting their well-being and quality of life.Public transit plays an important role in providing accessibility for socially disadvantaged groups. Many transit agencies are establishing partnerships with private companies to enhance their services, such as collaborating with technology companies to deliver on-demand transit services and collaborating with accessible taxi companies to increase the capacity of paratransit. Understanding the social impacts of these transit services is crucial. This dissertation aims to improve our understanding of the travel behaviour of socially disadvantaged groups, and how public transit serves these groups. It uses three empirical case studies to explore the travel patterns of people who use on-demand transit and paratransit, and investigate the social impacts of these services. In chapter 2, I conduct a case study in Belleville, Canada, with the aim of describing the user profile of on-demand transit and examining user satisfaction with the services. Additionally, the chapter shows there is a positive association between user satisfaction and activity participation among socially marginalized groups. Chapter 3 and chapter 4 place particular emphasis on people with disabilities, a significant subset of socially disadvantaged groups. The primary objective of these two chapters is to obtain a better understanding of the travel patterns of people with disabilities who use paratransit services. Chapter 3 investigates where and when individuals with disabilities travel using accessible taxis, and identifies the factors that influence wait and in-vehicle times. Chapter 4 categorizes paratransit users into five distinct groups, and investigates the changes in travel patterns before and during the COVID-19 pandemic. This thesis offers significant insights into the understanding of how public transit serves socially disadvantaged groups and helps mitigate transport-related social exclusion. It also has a practical contribution to policymakers seeking to enhance transport equity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.021 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".