Investigating the Effects of Mental Workload on Highway Safety
Bibliographic record
Abstract
The majority of vehicle collisions occur because of human error; in fact, studies have shown that approximately 95% of collisions are caused, at least in part, as a result of human mistakes. Therefore, it is important to study the main causes of human mistakes and develop mitigation strategies to reduce, if not eliminate, these errors from occurring. In this respect, designing highways that balance mental workload is a crucial task that ensures drivers have sufficient time to make appropriate decisions. However, the quantitative relationship between mental workload and collisions is not well documented in the safety literature. Enough evidence exists to support the reasonable conclusion that safety is affected by changes in workload, but there is no quantitative evidence of this effect. Consequently, this paper tries to investigate the relation between mental workload ratings and collisions on highways using data from Alberta, Canada. Horizontal and vertical curve parameters on two-lane, two-way highways were first extracted from LiDAR and GPS data using MATLAB algorithms, and the resulting features were summarized using Civil 3D. The mental workload ratings were assigned based on the presence of four major geometric features, namely, intersections, horizontal curves, vertical curves, and changes in cross-section. The results show that mental workload has a significant effect on safety for two-way, two-lane highways. Furthermore, the findings strongly indicated the need to integrate mental workload into the design process, to not only meet the operational needs of the highway, but also ensure that the geometric layout does not mentally overwhelm drivers.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".