Development of a Theoretical Delay Model for Heterogeneous and Less Lane-Disciplined Traffic Conditions
Bibliographic record
Abstract
In developing countries with limited or no availability of traffic sensors, theoretical delay models are the most commonly used tool to estimate control delay at intersections. The traffic conditions in such countries are characterised by a large mix of vehicle types and limited or no lane discipline (Heterogeneous, Less Lane-Disciplined (HLLD) traffic conditions), resulting in significantly different traffic dynamics. This research develops a queueing theory-based theoretical delay model that explicitly incorporates HLLD traffic conditions’ characteristic features like lack of lane discipline, violation of the First-In-First-Out rule, and a large mix of vehicle types. A new saturation flow-based Passenger Car Equivalent (PCE) estimation methodology to address heterogeneity and a virtual lane estimation approach to address lack of lane-discipline are proposed. The developed model shows 64% lesser error in average control delay estimation compared to the in-practice delay estimation models under HLLD traffic conditions. The developed model is used for signal optimisation under HLLD traffic conditions and reductions of up to 24% in control delay in comparison to the in-practice signal timing approach are observed. The study also highlights the significance of knowing the variation of delay in addition to average delay and presents a simple approach to capture the variation in delay.
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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 | 0.000 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".