Joint Confidence Region Approach to Ranking Hotspot Locations Considering Uncertainty in Expected Risk Estimates
Bibliographic record
Abstract
Network screening or crash hotspot identification is an essential task of all road safety improvement programs. The most common approach to network screening is to use statistical models to predict the expected risk at the locations of interest and then rank them accordingly. The predicted risk used for ranking is mostly in the form of point estimates, without any consideration of the inherent uncertainty with the estimates, which could lead to identifying a wrong list of crash hotspots. This study aims to fill this research gap by employing a frequentist approach to finding a joint confidence region of risk for ranking locations and identification of hotspots. A case study on three-legged minor approach stop-controlled intersections in Kitchener, Ontario, is conducted to illustrate the proposed approach. Crash risk is modeled using a combination of a hierarchical full Bayesian negative binomial model and a multinomial logit model, which are then used to estimate the 95% confidence interval of the expected risk. For each location, the confidence region of rankings is obtained on the basis of the expected risk estimates. The results show that considering uncertainty in the crash hotspot identification process can lead to varied ranking positions for each location. In fact, considering uncertainty, the true value of the estimated crash risk is unknown. By quantifying uncertainty, it can be concluded that the true value of the estimated risk follows a distribution with different probabilities. As a result, consideration of uncertainty in the road safety analysis may help to identify hotspots more accurately.
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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 | 0.019 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".