Considering Space Syntax in Bicycle Traffic Assignment with One or More User Classes
Bibliographic record
Abstract
Modeling bicycle traffic assignment requires consideration of the various factors and criteria that could play a role in a cyclist’s route decision-making process. However, existing studies on bicycle route choice analysis tend to overlook the less tangible or measurable aspects of cyclist route decision-making, such as a cyclist’s cognitive understanding of the network and a cyclist’s biking experience. This study explores the applicability of space syntax as a route cognitive attribute in a bicycle traffic assignment model. Since space syntax is a tool that links urban spatial layout to human movement, the results of a space syntax model can be used as a cognitive attribute for modeling bicycle movements with explicit consideration of the cognitive complexities of navigating through the environment. In developing a bicycle traffic assignment model, we considered relevant attributes such as route cognition, distance, and safety and integrated multiple user class analysis to reflect different biking experience levels. Numerical experiments using the Winnipeg network are conducted to demonstrate the applicability of the proposed bicycle traffic assignment model with one or more user classes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".