Modeling cyclists speed at signalized intersections: Case study from Ottawa, Canada
Bibliographic record
Abstract
The study of cyclist behavior for developing realistic and reliable behavioral models is attracting research focus. Achieving a detailed understanding of cyclist behavior is a cornerstone in building micro-simulation models and ultimately creating a more sustainable transportation system. Cyclist behavior is especially important at traffic intersections due to the exposure to turning and crossing vehicle movements. This study focuses on cyclist speed modelling at traffic intersections in urban areas. Data collection was conducted at three different traffic intersections in the downtown area in Ottawa, Canada. Video monitoring covered cyclists, vehicles, and pedestrian movements. Cyclists approached these intersections through physically segregated bike lanes. Cyclist speed was measured based on metric measurements at the intersections and temporal measurements from the video data. The variables associated with cyclist speed that were examined are: pedestrian crossing movements, adjacent vehicle traffic, traffic signal indication, type of right-turn lane, potential conflicts with turning vehicles, and occurrence of traffic violations. Multivariate regression analysis was conducted to link cyclist speed and the explanatory variables. The resulting R2values were 0.43 for predicting cyclist crossing speed and was 0.56 for predicting the change in cyclist speed while approaching the intersection. A number of statistically significant associations were observed and documented. Overall, considering the data collection effort, sample size, and the predictive power of this regression model, it can be concluded that the developed models are of potential practical use in predicting cyclist crossing speed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".