Development of a cycling data model: City of Vancouver case study
Bibliographic record
Abstract
This paper presents a general framework for a modeling platform and a visualization tool for bicycle volume data of different quality and quantity. The modeling platform is aimed to estimate the annual average daily bicycle traffic (AADB) on links where bicycle volume data are collected during part of the year even if very limited data exist. The visualization tool, on the other hand, displays the estimated AADBs along with their associated quality indices on a digital network map so that it becomes available to both officials and end users. This paper describes the general structure of the model along with the estimation algorithms used in different stages. The assumptions associated with model development are discussed along with their implications. A case study is presented and is referred to as Vancouver Cycling Data Model. It was shown that the model could lead to a coverage ratio of more than 70% using an initial dataset that included only 5% of the total number of hourly volumes that are actually needed to calculate the AADBs. This demonstrates the efficiency of the model in expanding the estimation of AADB over the entire network using limited data. This research effort is one of only few existing studies that attempted to develop cycling data models that can be used as useful decision-making tools for planners and sustainable transportation experts.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".