A Decision Support Tool for Accommodating Right-Turning Trucks at Urban Intersections in Walkable Communities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many North American jurisdictions are creating walkable urban environments through compact Complete Streets (CS) geometric designs, while not addressing the mobility and accessibility of goods despite the essential role goods movement plays in sustaining the liveability of the community. Prescriptive curb radii limits recommended by CS guidelines to lower pedestrian crossing distances may not adequately accommodate the right-turn maneuver of a truck. The paper develops a performance-based decision support tool to guide the design of urban intersection curb radii that facilitate the safe and efficient accommodation of trucks and pedestrians. The decision support tool relies on a novel Freight-Walkability relationship to define the context of urban intersections and establishes a curb radius design domain. A case study demonstrates the quantification of the Freight-Walkability relationship (in terms of peak hour right-turning truck volumes and a proposed Walkability Index) and the application of the tool at an existing intersection in Winnipeg, Canada. The tool helps transportation engineers and planners balance the mobility needs of trucks and pedestrians through short-term street-level design changes and long-term land use transformations.
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.
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 | 0.001 | 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 it