Machine Learning and Reverse Methods for a Deeper Understanding of Public Roadway Improvement Action Impacts during Execution
Bibliographic record
Abstract
The execution of public roadway maintenance, rehabilitation, and restoration activities disturb normal traffic flows, resulting in roadway capacity reduction, inducing travel time delays, and promoting traffic safety concerns. While they improve public roadway performance once complete, the impacts endured in executing these actions is significant. This work seeks a deeper understanding of the effects of improvement actions on traffic by juxtaposing their effects against those arising from traffic incidents that cause similar capacity reductions and related negative externalities. This is accomplished through direct and reverse comparisons with traffic incident impacts. A measure of unit delay that uses observations to determine event location extent, duration, and propagation direction was computed at both facility and corridor-wide levels to establish the degree to which improvement actions and traffic incidents are similar or dissimilar. Alternative hybrid machine-learning methods are proposed to identify and contrast those traffic characteristics that contribute greatest to correct detection of each type of downtime event. These techniques can detect traffic events and accurately distinguish between event types (whether a collision or improvement activity). The techniques were applied on seven months of data obtained from 2019 along three corridors from northern, southern, and western regions of the Commonwealth of Virginia. Those traffic characteristics that contribute greatest to correct event detection of each event type were identified and their similarities and differences were studied. General linear, multivariate regression equations were also developed for more general application.
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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 | 0.000 | 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 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".