Modeling Sustainable Traffic Behavior: Avoiding Congestion at a Stationary Bottleneck
Bibliographic record
Abstract
Sustainable traffic behaviour is increasing in importance as traffic volume rises due to population growth. In this paper, a model for traffic flow at a stationary bottleneck is developed to determine the parameters that cause congestion. Towards this goal, traffic density, speed, and delay were acquired during peak and off-peak periods in the morning and afternoon at a stationary bottleneck in Peshawar, KPK, Pakistan. The morning and afternoon peak periods have high densities, low speeds, and considerable delays. Regression models are developed using this data. These results indicate that there is a linear relationship between density and time at the stationary bottleneck and a negative linear relationship between density and speed. Thus, an increase in density increases the time delay and reduces the speed. I comprehensive traffic delay model is characterized by a stationary bottleneck. The Kolmogorov-Smirnov (KS) test and P-values were used to identify the best-fit distribution for speed and density. The binomial and generalized extreme values are considered the best fits for density and speed. The results presented can be used to develop accurate simulation models for stationary bottlenecks to reduce congestion. Doi: 10.28991/CEJ-2022-08-11-02 Full Text: PDF
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".