Transferability of Community-Based Macrolevel Collision Prediction Models Between Different Time-Space Regions for Road Safety Planning Applications
Bibliographic record
Abstract
This paper describes the use of several recently developed community-based, macro-level collision prediction models (CPMs) and model-use guidelines in a CPM transferability case study between spatial-temporal regions. The objective was to test the model-use guidelines in an application that involved transferring CPMs developed using 1996 data for the Greater Vancouver Regional District (GVRD), for use in the Central Okanagan Regional District (CORD) using 2003 data. The GVRD and CORD are regions located 400 kilometers apart in the Province of British Columbia, Canada. The case study was carried out in two parts. First, CPMs were developed ?from scratch? using 2003 data for the City of Kelowna following recommended community-based, macro-level collision prediction model development guidelines. Second, existing CPMs were transferred from the GVRD to the CORD, using the recommended transferability guidelines. An analysis of the results revealed that macro-level CPM transferability was possible and no more complicated than micro-level CPM transferability. To facilitate the development of reliable community-based, macro-level collision prediction models, it is recommended that CPMs be transferred rather than developed from scratch whenever and wherever communities lack sufficient data of adequate quality.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".