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Record W4406722562 · doi:10.1016/j.trip.2024.101314

Equitable transportation and resilience hubs: Analysis of underserved population needs, usage, and travel behaviour

2025· article· en· W4406722562 on OpenAlex
Thayanne Gabryelle Medeiros Ciríaco, Syeda Narmeen Zehra, Veronica Wambura, Stephen D. Wong

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueTransportation Research Interdisciplinary Perspectives · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsResilience (materials science)BusinessPopulationTransport engineeringEnvironmental healthMedicineEngineering

Abstract

fetched live from OpenAlex

• We assessed resilience hub usage and mode by underserved groups from survey data. • Underserved individuals would be likely to use a resilience hub during emergencies. • Statistical differences between underserved and non-underserved groups varied. • Certain intersecting groups exhibited unique and significant hub usage patterns. • Varied mode choice to/from hubs indicates more multi-modal design and planning. Extreme weather events and other hazardous events often require a range of strategies to safely shelter people, distribute resources, and facilitate recovery efforts. This is particularly important for underserved populations who usually lack reliable access to shelters, transportation, and social networks. To begin addressing these problems and increase community capacity, resilience hubs – physical locations that support residents in emergencies and everyday conditions – have emerged as a possible equitable strategy. Despite potential benefits for underserved populations, research and practice have yet to consider how different demographic groups will use or travel to/from these hubs. To address these gaps, we conducted an empirical study using survey data from 950 respondents in the Edmonton Metropolitan Region in Alberta, Canada. Of these respondents, we focused on several underserved groups. Simple descriptive statistics and statistical tests were used to understand the groups’ needs and observe similarities and divergences between groups. We also calculated spatial statistics to identify how mode choices varied with people’s preferred resilience hub locations. We found a high willingness of groups to use resilience hubs, especially in emergency conditions. However, differences between groups and with non-underserved groups were relatively moderate and varied. Respondents prioritized a range of basic services along with transportation-related elements, including accessibility for individuals with disabilities, transit connections, parking, and walkability. Moreover, our mode choice analysis highlighted the necessity of incorporating multimodal transportation options to resilience hubs. We offer several policy recommendations that inform the equitable development of resilience hubs, including the importance of local placement and needs-based services.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.056
GPT teacher head0.431
Teacher spread0.376 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it