Delay Model for Signalised Intersections Based On M/D/N Queueing System
Bibliographic record
Abstract
Delay is a fundamental measure for evaluating the performance of the traffic systems.However, the stochastic nature of vehicle arrivals and discharges at the intersection introduces significant complexity in its estimation.Its estimation becomes even more challenging in developing countries like India, where traffic exhibits mixed and lane-free (MLF) characteristics.Most of the delay models are derived based on the queuing theory concepts and traditionally considered the queue at a signal as M/D/1, indicating random arrival, deterministic discharge and one server per lane.This study considers the scenario with multiple servers to cater to the parallel movement of vehicles under MLF conditions, making the queuing system as M/D/n.Three different approaches to incorporate this, along with the server with vacation concept, are developed and compared in this study.The models were evaluated using VISSIM simulations on validated networks of two Indian intersections: Mhalgi Nagar in Nagpur and Kaiveli in Chennai.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".