Bibliographic record
Abstract
Visibility is one of the basic requirements for safe driving. Any type of vision obstruction can interfere with the driver’s ability to operate a vehicle and thus poses a significant risk to road traffic safety. In addition to widely investigated adverse weather conditions such as rain, fog, and snow, which can reduce visibility, bright sunny days may also bring challenges to safe driving due to the phenomenon of sun glare. Although much evidence has been found to support that sun glare can highly impair one’s visual performance, there are significant gaps in the knowledge and methodology dedicated to examining and quantifying the sole effects of sun glare on road safety. The overall objective of this thesis is to gain a better understanding of the risks on the road network posed by sun glare and evaluate its safety effects in the city of Edmonton. To achieve this objective, a two-stage analysis was conducted. The first stage developed a methodology to model sun glare occurrence for the road network in the city, aiming to identify when and where a driver would be exposed to the risk of sun glare by considering different factors associated with the sun’s position and road geographical conditions. Consequently, potential sun glare time windows were demarcated for each month over a typical year and corresponding glare prone locations were identified and plotted in a series of visualization maps, which can provide a reference to the public for identifying locations where sun glare is most prominent in the city of Edmonton. After identifying the glare prone locations, the safety risks at those locations during sun glare periods were assessed in the second stage. By contrasting the number of collisions during glare and non-glare conditions, this second stage aimed to quantify the sole effects of sun glare on road collisions. A case-control design method was used to control for potential confounding factors related to weather condition, collision time/location, and travel direction. To test the significance of the differences in collision numbers, several statistical techniques were employed and included: chi-square test of independence, configural frequency analysis, and the Wilcoxon signed rank test. Ultimately, three major findings were concluded from the results of the collision analysis. First, sun glare was found to significantly contribute to collision occurrence, especially at road intersections. Quantitative assessments showed that collisions were expected to increase by about 30% under the condition of sun glare. Second, sun glare effects during mornings and evenings were observed to be especially worse in the months of spring and fall. Also, most of the daytimes in January, November, and December, traffic safety in the southbound direction was significantly affected by sun glare. Lastly, certain collision maneuver types were over-presented during sun glare. It was found that the collisions due to signal violations and failing to yield to pedestrians and cyclist were over-presented at intersections. At mid-block locations, the proportions of collisions due to improper turning and lane changes were observed to be significantly higher. Overall, the approaches proposed and developed in this thesis provide a new and innovative method to quantify the effects of sun glare on road safety. By linking sun glare exposure modeling with a thorough glare related collision assessment, this research helps to provide additional insights about the extent by which sun glare affects road safety. The findings from this thesis can be used to assist in alleviating the risks associated with sun glare in the planning and designing of future roads.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".