A Methodology for the Evaluation of Street Functions Using Video Data: A Case Study on Speed Humps in Montreal
Bibliographic record
Abstract
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.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".