Developing vehicular and non-vehicular trip generation models for mid-rise residential buildings in Kelowna, British Columbia: Assessing the impact of built environment, land use, and neighborhood characteristics
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.
Bibliographic record
Abstract
This study develops vehicular and non-vehicular trip generation models for mid-rise, multi-family residential developments. A comparative analysis of observed and Instiutue of Transportation Engineers (ITE) trip rates suggests that ITE rates consistently overestimate. A latent segmentation-based negative binomial (LSNB) model is developed to improve the methodology for estimating vehicular and non-vehicular trips. One of the key features of an LSNB model is to capture heterogeneity. Segment allocation results for the vehicular and non-vehicular models suggest that one segment includes suburban developments, whereas the other includes urban developments. Results reveal that a higher number of dwelling units is likely to be associated with increased vehicle trips. For non-vehicular trips, a higher number of dwelling units and increased recreational opportunities are more likely to increase trip generation. The LSNB model confirms the existence of significant heterogeneity. For instance, higher land-use mix has a higher probability to deter vehicular trips in urban areas, whereas trips in the suburban areas are likely to continue increasing. Higher density of bus routes and sidewalks are likely to be associated with increased non-vehicular trips in urban areas, yet such trips are likely to decrease in suburban areas. An interesting finding is that higher bikeability in suburban areas is more likely to increase non-vehicular trips. The findings of this study are expected to assist engineers and planners to predict vehicular and non-vehicular trips with higher accuracy.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 it