Bibliographic record
Abstract
This paper will look to identify how the built environment can affect collisions at intersections. The Regional Municipality of York was used as the study area. York Region has areas of high-density traffic as well as rural regional road. Due to York Region’s proximity to Toronto there is also commuting traffic during rush hours. A literature review looked into different studies of traffic collisions. A focus of many was human factors, such as impaired driving, distracted driving and inexperience to name a few. For this paper, the focus is on the built features and how different design components of on intersection can affect the number of collisions. Using information from the literature review data was gathered for different built environment features, i.e. intersection type, bus stops and red-light cameras. Data was also gathered for collisions that occurred in York Region, this included the location, time of day, day of the week, and initial impact type. To evaluate how these features effected the number of collisions at an intersection, descriptive statistics, linear regression and qualitative analysis was used. The descriptive statistic shows an overview and percentage of accidents that occurred in separate groups. These groups include property damage, injury, and fatal accidents, traffic control types, and intersection types. Linear regression was used to determine which factors were increasing the number of accidents and which were helping to decrease accidents. Finally, qualitative analysis was used to study the intersections that had the top ten number of accidents that were fatal or injury. After completing the analysis, a case study was conducted on three intersections, one that has seen an increase in accidents one that has seen a decrease in accidents and finally one that has consistently had a high accident count. From all the information and analysis conclusions and recommendations were put forward to help improve road safety in York Region.
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 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".