Validation of Machine Learning Algorithms as Predictive Tool in the Road Safety Management Process: Case of Network Screening
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Safety performance functions (SPFs) are the key regression tools in the road safety management process (RSMP) and are used to predict crash frequency given a set of roadway and traffic factors. Although regression-based SPFs have been proven to be reliable tools for road safety predictive analytics, some limitations and constrains have been highlighted in the literature, such as the need to assume a probability distribution, the need to select a predefined functional form, possible correlation between independent variables, and possible transferability issues. An alternative to traditional regression models as predictive tools is the use of machine learning (ML) algorithms. This research compared the prediction performance of three well-known ML algorithms, i.e., support vector machine (SVM), decision tree (DT), and random forest (RF), with that of traditional SPFs, and applied and validated ML algorithms in network screening, which is the first step in the RSMP. To achieve these objectives, traditional SPFs using negative-binomial (NB) generalized linear regression were estimated and compared with ML algorithms using three different goodness-of -fit criteria. A data set of urban signalized and unsignalized intersections from two major municipalities in Saskatchewan (Canada) was considered as a case study. Ranking consistency tests of collision-prone locations identified using ML-based and SPF-based performance measures were conducted. The results showed that the consistency of ML-based measures in identifying hotspots was comparable to that of SPF-based measures, particularly the excess (predicted and expected) average crash frequency. Overall, the results of this research support the use of SVM, DT, and RF as predictive tools in network screening.
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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 | 0.000 |
| 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 it