Improving road network databases by integrating a geographic information system and digital imagery
Bibliographic record
Abstract
Road lighting information is an important record in road network databases. It has been frequently observed that such data are either missing or not updated due to the high data collection cost using traditional methods. This paper presents a new methodology for identifying and (or) resolving missing and conflicting road lighting data in road network databases. The methodology is based on: (i) integrating the single-line road network (SLRN) in a geographic information system format with a road network database and (ii) integrating the SLRN with a collision database. Missing and conflicting lighting data are resolved using a semi-automatic method for extracting streetlight pole information. The integrated system can also identify inconsistencies related to short segments and segments with mixed illumination characteristics. Inconsistencies in the traffic volume database were also examined and the effect of data inconsistency on safety performance functions was evaluated. The proposed methodology represents an inexpensive, efficient tool for improving the quality of road network databases and associated road safety analysis.
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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 | 0.002 |
| 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".