A Methodology for the Evaluation of Street Functions Using Video Data: A Case Study on Speed Humps in Montreal
Why this work is in the frame
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Bibliographic record
Abstract
The direct observation of cars, pedestrians, cyclists, and other street users can be a viable method to evaluate the three main street functions, namely mobility, access, and place. However, a systematic procedure to evaluate the street functions is not evident in published work. Previously, a comprehensive framework for street functions and all users was proposed without any application. The aim of this research is therefore to develop a systematic methodology for collecting, pre-processing, and analyzing data on street users based on that comprehensive framework and to use it in a case study. In the proposed methodology, the trajectories and types of street users, their instantaneous speed, and direction of movement are automatically extracted from the collected videos using video analytics. These data are then analyzed in a new software tool, the Studio application, to derive street function evaluation indicators. The proposed method is applied to comprehensively assess the changes after speed hump installations in four residential streets in Montreal, Canada. The results demonstrate the value of direct street user observation and the proposed semi-automated method. The empirical results of the proposed method show that the speed of cars has decreased by 20-30% at all sites, while there have been significant changes in the flow and characteristics of vehicles, cyclists, and pedestrians in the study areas.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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 it