Bibliographic record
Abstract
The main objective of this thesis is to develop microscopic accident prediction models for estimating the safety potential of 4-leg signalised intersections in the City of Vancouver, B.C. and describes the applications of these models in traffic safety analysis. The aim, therefore, is to examine the traffic variables that appear to underlie the occurrence of accidents of these intersections and explain, in a statistical sense, the generation of accidents as a function of these variables. Generalised linear regression was employed to develop the models because of its superiority over conventional linear regression in modelling accident occurrence. The statistical software package GLIM4 was used to accomplish this task. The study made use of a sample of 8466 accidents that occurred at 170 4-leg signalised intersections during the years of 1994-1996. The data on accident frequencies and traffic volumes were obtained from the City of Vancouver. Several models that have different applications in the field of traffic safety were developed in this study for the 4-leg signalised intersections of the City of Vancouver. Different error structures that can be utilised to model the relationship between accidents and traffic flows are reviewed. Microscopic models for different accident types were developed in conjunction with macroscopic models for Total, Severe and Property Damage Only accidents. The microscopic models are presented in conjunction with the three macroscopic models which all resulted in statistical significance. Several model applications are discussed. Examples of how to obtain location-specific safety estimates, how to identify accident prone locations, how to rank the accident prone locations, and how to conduct a before and after safety evaluation are presented. Microscopic and macroscopic models are used simultaneously to determine which intersections should be regarded as accident prone locations according to specific accident patterns that can be effectively treated by engineering countermeasures.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".