Safe Accommodation of Active Transportation Through Highway Interchanges – Balancing the Needs of Cyclists, Pedestrians, and Motorists.
Bibliographic record
Abstract
The research project was accomplished through five interrelated tasks. A literature review and jurisdictional scan revealed a strong interest in the topic in Ontario and elsewhere. In particular, municipalities have a strong desire for guidance that reflects their desire to implement design and operational features that may be at odds with provincial policies. The literature review also revealed research of direct relevance not only to designs for accommodating vulnerable road users (VRUs) at interchanges, but also to the tools for assessing these designs from both safety and operational perspectives. The promise of one such tool – microsimulation – was explored and demonstrated. Other tools, including design flag assessment and Safe Systems Index assessment, were explored through case studies based on actual interchanges in Ontario. That investigation revealed that these tools may feasibly be used to evaluate designs for the safe accommodation of VRUs at interchanges. This outcome was confirmed through the exploration of a decision matrix, which revealed that existing guidance provided for MTO can serve as a useful starting point for selecting possible interchange design alternatives. Design flags assessment and Safe Systems index assessment can then be used as supplemental decision-making tools for comparing alternatives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".