Travel Behaviour and Community Needs for Resilience Hubs
Bibliographic record
Abstract
Communities continue to experience significant and damaging disasters, which has prompted governments to devise solutions to protect lives and reduce overall impacts. One emerging strategy is the development of resilience hubs, which can serve the community during disasters and everyday conditions. However, most research and guidance for resilience hubs remain largely theoretical and do not account for community needs. Moreover, research and practice have not fully integrated transportation into resilience hub design, such as how travel to and from resilience hubs is facilitated. Consequently, we conducted an empirical study leveraging statistical tools and models using data from a survey of Edmonton, Canada, residents (n = 950) conducted between November 2022 and February 2023. Through descriptive statistics and discrete choice models, we uncover important results related to resilience hub usage, transportation design, and mode choice in both normal and disaster conditions. Modelling results found a strong influence of household characteristics on the normal usage of resilience hubs, while individual characteristics were more influential on hub usage as a temporary shelter. No clear patterns of variables influenced mode choice (travel to/from hubs), except the insignificance of resilience hub usage (i.e., trip purpose) for normal conditions. For mode, the results showed a strong preference for private vehicles, yet still a relatively high multi-modal split (e.g., walking, transit, shared mobility). Residents also preferred highly localized resilience hubs with a variety of transportation options, services, and amenities. Using these results, we provide a series of practice-oriented recommendations for communities in the design and operations of resilience hubs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".