Bibliographic record
Abstract
Urban transportation planning has traditionally focused on capacity and congestion issues with some attention paid to operation and management and with the treatment of such issues typically made proactively. In contrast, road safety has received little attention in the planning process. Safety-conscious planning is a new proactive approach that incorporates safety issues into the transportation planning process. This approach requires a safety planning decision-support tool to facilitate a proactive approach to the assessment of safety implications of alternative network planning initiatives and scenarios. The objective of this research study is to develop a series of zonal-level collision prediction models that are consistent with conventional models commonly used for urban transportation planning. A generalized linear regression modeling approach with the assumption of a negative binomial error structure was employed for exploring relationships between collision frequency in a planning zone and some explanatory variables such as traffic intensity, socioeconomic and demographic factors, land use, and traffic demand measures. Planning-level safety models developed in this study with data for the city of Toronto, Canada, are presented with illustrative applications of how they can be used as decision-support tools for planners to explicitly consider safety in the transportation planning process. Macrolevel collision modification factors are presented to illustrate how the models can be used to examine the impact of each individual planning variable on the safety of an urban zone.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".