A cordon count program for pedestrians and bicycles commuting to/from a winter city university campus
Bibliographic record
Abstract
Pedestrian and bicycle traffic monitoring programs generate data that can be used to improve safety, promote healthy lifestyles, and improve design of non-motorized facilities. Traffic monitoring is well established for motorized vehicles, but is still developing for pedestrians and bicycles (non-motorized modes). Specifically, there is a need to establish systematic and flexible monitoring programs that capture the unique travel patterns of pedestrians and bicycles for various land uses and demographics. The purpose of this research is to design and implement a pedestrian and bicycle cordon count program in the context of a university campus in a winter city. The approach to the research was to design the data collection plan, collect and process the data, and analyze it to calculate the simultaneous cordon counts, daily volumes, and average weekday traffic statistics at each data collection site. Automatic equipment counts and manual counts by video were used to collect data throughout the year to determine patterns for each season-semester combination. Findings provide insight into modal, temporal, and spatial characteristics of pedestrians and bicycles at the University of Manitoba Fort Garry campus. Specifically: 1) pedestrian patterns seem more affected by semester than weather, 2) bicycle patterns seem more affected by weather than semester, and 3) the spatial distribution of traffic does not remain constant at the sites throughout the year. The research assists jurisdictions in planning data collection programs for similar urban activity areas, develops a novel approach to pedestrian and bicycle traffic data collection within a cordon count program, and provides data inputs for transportation infrastructure planning and design decisions in the campus area.
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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 | 0.000 | 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 | 0.000 | 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".