Advancing the Methodology for Estimating the Effects of Safety Treatments
Bibliographic record
Abstract
<p>The Highway Safety Manual uses safety performance functions (SPFs) and crash modification factors (CMFs) to quantify the safety effects of a roadway design and operational changes. A number of substantial methodological issues in SPF and CMF estimation exist that have not yet been completely addressed. Notably, although applicability of SPFs and CMFs may significantly vary by crash severity, only limited consideration is given to severity distribution. Moreover, when developing crash modification functions (CMFunctions), most of the current state-of-the-art studies only rely on the functional forms that may not address the non-linear effects of roadway attributes on safety. In addition, while many studies advocate using traffic conflicts as surrogate measures of safety to address some issues in CMF estimation, most of the current measures do not sufficiently take into account the severity of conflicts.</p> <p>The thesis is based on an amalgamation of four research papers that separately delve into these topics in Chapters 3 to 6. Chapter 3 develops and compares alternative approaches for predicting number of crashes for each severity level. The applicability of a two-stage SDF (severity distribution function) modeling approach is the main focus of this chapter. Chapter 4 applies Bayesian Markov Chain Monte Carlo (MCMC) simulation to investigate the functional forms used to derive CMFunctions in cross- sectional regression models. This chapter mainly focuses on the methodology and uses freeway median width as a case study. Chapter 5 aims to demonstrate the versatility of the approach by applying that to passing lanes on two-lane roads. The results of these two applications highlight the importance of using the functional forms that can capture non-linear effects of road attributes for CMF estimation in cross- sectional models, while providing robust CMFs for practical applications.</p> <p>Chapter 6 investigates a novel traffic technique using the data obtained from video observations at signalized intersections. The measures of post encroachment times and corresponding conflicting vehicle speeds are integrated to define a risk score which classifies conflicts into three severity levels. The results show strong relationships between the classified conflicts and both total and fatal/injury crashes and conform the promise of the approach for estimating CMFs where there are insufficient crash data for directly estimating them.</p>
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".