Development of safety performance functions and a GIS based spatial analysis of collision data for the City of Saskatoon
Bibliographic record
Abstract
The American Association of State Highway and Transportation Officials (AASHTO) produced the first edition of the Highway Safety Manual (HSM) in 2010. The HSM introduces a six-step safety management process which provides engineers with a systematic and scientific approach to identifying and managing safety concerns on a road network. Each step plays a vital role in improving the safety of target road networks, and the first step, network screening, is the step where the safety issues are first identified. This is accomplished through the use of a series of Safety Performance Functions (SPFs). SPFs are mathematical equations that relate the collision frequency at a particular location with traffic volume and roadway characteristics. The purpose of this research is to develop a series of locally derived SPFs for the City of Saskatoon to allow engineers to estimate the expected number of collisions for the purpose of evaluating new roadway design alternatives and screening the existing roadway network in terms of safety. By developing locally derived SPFs, it may be possible to obtain a better prediction of the expected number of collisions than from using the base model SPFs provided in the HSM. The development of SPFs required three separate databases containing roadway characteristics, traffic volume, and collision records to be integrated into a single database. Using the statistical program R-Language, SPFs were developed and validated for the City of Saskatoon. The developed SPFs were used to conduct a network screening of Saskatoon’s roadways and intersections to identify locations with safety concerns. The results from the network screening were incorporated into ArcGIS to allow for a visual analysis of the spatial collision patterns. Finally, the locally derived SPFs were compared with the HSM base model SPFs to determine if other jurisdictions would benefit from developing their own locally derived SPFs for urban areas.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.007 | 0.001 |
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".