Design Consistency and Multi-modal Safety in Urban Areas
Bibliographic record
Abstract
An important component of road safety is the compatibility between intended operations of a facility and how drivers actually interpret and react to in-field geometric and traffic control characteristics. Individual design elements may meet or exceed minimum standards, but safety issues may still exist if the geometric characteristics are not fully consistent with signage and markings. At intersections in urban areas, safety issues can be compounded by impacting not only motor vehicle safety but also influencing the safety of pedestrians and cyclists. Specific issues can arise when turning geometry is not fully compatible with the traffic control scheme. For example, a channelized right turn that has yield control, but is otherwise consistent in geometric design with an added merge lane (due to, for example, a large turning radius), can result in conflicts due to variable driver expectations, with some drivers braking (as per the yield control) while others accelerate to merge at speed, which can create speed differentials and an increase in rear end and merging collisions. Issues can also arise where the safe travel speed on an urban roadway alignment does not align with driver expectation. This paper identifies common issues that can result in conflicts and collisions between motorists in urban areas due to compatibility issues with respect to turn movements as well as roadway approaches and alignments. Case study examples from Alberta and British Columbia are presented, where the safety issue is identified (for vehicular as well as non-motorized travel modes), the reasons (as possible) that the compatibility issue may exist in the first place, along with mitigation options that can be considered (while highlighting potential benefits of those improvements). Finally, conclusions and lessons learned are summarized
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".