Development of ‘speed ratio’ based level of service criteria on undivided urban streets in mixed traffic context
Bibliographic record
Abstract
Developing countries are facing challenges in sustaining urban traffic congestion due to rapid urbanization. To manage urban traffic, a traffic engineer first needs to assess the current operational condition on urban roads. Level of service (LOS) is used to define the operational traffic condition within a traffic stream in terms of service quality that a facility is providing to its user. This paper proposes a novel approach to estimate ‘speed ratio’ by considering individual free-flow speed (FFS) of different vehicle categories. This study also provides a comparison between FFS estimated using the methods given in Highway Capacity Manual 2010 and Indian-Highway Capacity Manual 2018. LOS criteria were developed using five clustering technique: K-means, K-medoids, Clustering Large Applications, Fuzzy-C Means, and Hierarchical Agglomerative Clustering. Both internal and external cluster validation indices were used to find the optimal number of clusters and suitable clustering algorithms.
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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".