QUANTITATIVE APPROACH TO DETERMINE PEDESTRIAN DELAY AND LEVEL OF SERVICE AT SIGNALIZED INTERSECTION
Bibliographic record
Abstract
Purpose: Level of Service is a widely adopted terminology to determine the efficiency of any transport system. From the literature it was studied that the multiple linear regression models established by many researchers to determine PLoS evolved with addition or removal of one or more physical parameters or with respect to the perception of users from different locations. At an intersection, there is little or no established methodology developed so far to determine a quantitative approach for PLoS similar to Vehicular Level of Service (VLoS). It was also pointed out that under heterogeneous traffic conditions, pedestrians are most vulnerable at intersections and they share the same space with motorized vehicles for crossing movements. Methodology: Thus, this study was built on the hypothesis that pedestrian delay of a signalized intersection is quantitatively dependent on pedestrian volume, vehicular volume and cycle time. Two signalized intersections operating as fully actuated and fixed cycle time were considered for study for period of four hours each, covering two hours of morning peak and off-peak hour traffic data. Main Findings: Using various statistical techniques, an empirical model was developed between the pedestrian delay and independent variables namely cycle time, pedestrian volume and vehicular volume. PLoS range was also determined through k-means clustering technique. Implications: The empirical model developed was validated and the application of this research was also explained. Novelty: The study is a new quantitative approach to determine PLoS and was limited to two intersections. Increase in the data may improve the accuracy of the model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".