A Reliability-Based Framework to Assess the Impacts of Increasing Freeways’ Posted Speed Limits
Bibliographic record
Abstract
Posted speed limits have a critical impact on highway safety and mobility. Increasing the posted speed limit may increase the risk of collisions and reduce the overall safety level. This study utilized a reliability-based framework to assess the safety impacts of increasing the speed limits on highways. Four highway segments were considered, including tangents, horizontal curves, and crest and sag vertical curves. For each segment, the suitable modes of noncompliance were evaluated at four speed limit scenarios (100, 110, 120, and 130 km/h) to quantify the increased risk associated with the speed limit increase. The results indicate serious negative implications of the speed limit increase, as the risk ratio corresponding to a 10 km/h increase in speed limit averaged between 1.09 and 1.75 on tangents with varying traffic volume conditions. On horizontal curves, the risk ratios of skidding and inadequate sight distance were around 1.43 and 1.73, respectively. The same 10 km/h increase in speed limits resulted in an average risk ratio of 1.74 and 1.49 for vertical crest and vertical sag curves, respectively. The framework was applied to assess a proposed increase in speed limits on a group of freeways in Ontario, Canada, from 100 km/h to 120 km/h. The results showcase the significant added risks using actual freeway parameters and characteristics collected from the field, despite the conservative road design. Furthermore, mobility assessment by means of microsimulation indicated little to no benefits of the speed limit increase in the studied section. The presented framework advocates a proactive safety approach for practitioners to evaluate speed limit changes before implementation. Additionally, the reported findings shed light on the safety risks of raising the freeway speed limits and refute the claims of enhanced mobility that are often used to promote such changes.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".