Development of a Procedure for Estimating Expected Safety Effects of a Contemplated Traffic Signal Installation
Bibliographic record
Abstract
The Manual on Uniform Traffic Control Devices contains warrants for traffic signal installation but cautions that satisfying a warrant does not in itself justify the decision to install a signal and that one should not be installed unless an engineering study indicates that this will improve the overall safety or operation of the intersection. The development of an easily implementable procedure, which is intended to be part of an engineering study, for estimation of the expected safety effects of a contemplated signal installation is reported. These effects can then be considered in conjunction with other impacts in a conventional economic evaluation. The development of the procedure by use of a multijurisdiction database is described, and a detailed illustration is presented. Use is made of the empirical Bayes methodology that of late has been recognized as the state of the art in safety estimation and of the most recent advances in that methodology. Substantial focus is placed on the application of that methodology and on the development of the accident prediction models required to support that application. The development of the procedure is part of an NCHRP project (NCHRP 17-16) that aims to improve the safety warrant for signal installation and, more generally, to determine how safety is considered in the decision to install or not install a signal.
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 | 0.013 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".