Incorporating the Effect of Special Events into Continuous Count Site Selection for Pedestrian Traffic
Bibliographic record
Abstract
This paper presents results from pedestrian monitoring research conducted in a dense urban environment in Winnipeg, Canada. Pedestrian counts were conducted in downtown Winnipeg using infrared pedestrian counters. Count sites were assigned to traffic pattern groups (TPGs) based on their response to special events occurring in the study area. Once these groups were established, eight continuous count sites were installed to initiate an ongoing pedestrian traffic monitoring program for the city. Traffic monitoring efforts have primarily focused on motorized travel. As more jurisdictions prioritize active transportation, addressing the need for network-level pedestrian data is essential to optimize engineering decisions. The first step to developing any system-wide traffic monitoring program is to define TPGs. These groups enable the spatial variation of short-duration counts to be adjusted to annual statistics by the temporal variation of similarly behaving continuous counts. Short-duration count sites were characterized by daily and hourly trends consistent with existing pedestrian traffic monitoring practices. Recognizing the influence of large evening events on pedestrian traffic, a metric was developed called the evening proportion ratio (EPR) to quantify the effect of special events. Based on the spatial distribution of EPR values, two TPGs were developed for downtown Winnipeg. These are the “urban utilitarian” and “urban utilitarian – event” groups. These groups were used to select continuous count locations for ongoing pedestrian traffic data collection. The importance of this research lies in its future applicability to other jurisdictions in developing a standard approach for urban transportation authorities to strategically implement pedestrian traffic monitoring programs.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".