Impact of Horizontal Curves and Gradient on the Speed of Heavy Vehicles on Two-Lane Rural Mountain Roads: Case of the Loja-Catamayo Road
Bibliographic record
Abstract
This study analyzes the speed behavior of heavy vehicles on rural mountain roads, specifically on the Loja-Catamayo road in Ecuador.This analysis is crucial for improving road safety as the operating speed of these vehicles is influenced by multiple factors, including road geometric design, topography, and weather conditions.The study focuses on evaluating the impact of curve radii and gradients on the speed of heavy vehicles.Data collection was performed using a high-precision GPS device installed in a follow-up vehicle that accompanied 14 trucks during their journeys on the road.The obtained data were analyzed using statistical methods, including normality tests and one-way ANOVA, considering two main variables: curve radii and gradients.The results show that both curve radii and gradients have a significant impact on the speed of heavy vehicles.Curves with radii less than 50 meters and steep gradients tend to reduce vehicle speed, indicating greater caution by drivers on these road segments.Additionally, it was found that vehicle speeds are higher on the outbound route (Loja-Catamayo) due to the terrain characteristics.The study's conclusions highlight the importance of considering these geometric variables in the design and maintenance of rural roads in mountainous environments to improve road safety.The findings provide valuable information for planning and implementing speed control measures and optimizing road design.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".