Evaluation of driver visual demand at different design speeds on complex two-dimensional rural highway alignments
Bibliographic record
Abstract
Road crashes are a major cause of loss of human life, property and money throughout the world. One of the reasons behind these crashes is the interaction between drivers and road alignments. The need to understand the factors that affect drivers has become obvious and is now being addressed by researchers. Moreover, driver workload is gaining attention as a measure of highway-design consistency as it directly reveals design features to the driver. This research focuses on evaluating driver visual demand at different design speeds along with other geometric design features for two-dimensional rural horizontal roadway alignments. Twelve such alignments having simple and complex curves were designed following the standards of the American Association of Highway and Transportation Officials (AASHTO) and the Transportation Association of Canada (TAC). The driver simulator at Ryerson University, Toronto, recently modified after the integration of a car, was used for the simulation of roadway alignments. Scenario Definition Language (SDL) was used to develop Event files for simulation and to save the required data. Twelve drivers drove the simulated alignments. The output data relating to driver visual demand were processed using MS Notepad and MS Excel. The visual demand calculations for full-element length (VDF), half-element length (VDH) and the first 30 m of element length (VD30) for curve and tangent sections of alignments were done using MS Excel. Statistical Analysis Software (SAS) was used to anlayze and develop models for VDF, VDH and VD30 for curve and tangent sections, first considering design speed only as explanatory variable and then considering design speed along with other geometric design characteristics as explanatory variables. It has been observed that visual demand increases with the increase in design speed. Besides, the combined effect of design speed an other geometric design characteristics (e.g., the type of preceding element, the turning direction of a curve) has significant effect on visual demand. It was also found that visual demand followed a Log Normalized distribution which was also observed by previous research. The developed models were used to establish the visual demand profile for highway design consistency evaluation. The comparison of visual demand profile and operating speed profile has shown that the visual demand can be an acceptable measure for evaluating the highway design consistency.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".