Evaluating pedestrian perceptions of street design with a 3D stated preference survey
Bibliographic record
Abstract
In many places, streets are still primarily designed for the convenience of motorists, considering mobility function as the principal design goal. There is a scarcity of empirical evidence on the relationship between the design of a street and how it is experienced by pedestrians who use it. This work focuses on quantifying pedestrians’ perception of walkability through a stated preference survey using a dynamic 3D representation of various street designs in Toronto, Canada. The stated preference scenarios are generated through a rule-based 3D environment (Esri’s CityEngine) and animated using a gaming engine (Unity). A random sample of 600 Torontonians is used for the empirical investigation by estimating a mixed multinomial logit model. The results indicate that there is a high preference for (i) streets that include transit lanes as opposed to car-exclusive lanes, (ii) the presence of trees on the sidewalk, and (iii) two-way cycle paths on the curb lane. Furthermore, pedestrians are willing to trade sidewalk width for the presence of trees and outdoor dining. The survey’s innovative presentation mode and its findings can contribute to the development of much-needed evidence-based design tools to assess the trade-offs required between the many possible uses of roadway space, while focusing on the overlooked role of the pedestrian experience.
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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 | 0.001 |
| 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".