Traffic Signal Timing Optimization by Modelling the Lost Time Effect in the Shock Wave Delay Model
Bibliographic record
Abstract
Over the years, studies presented on shock wave model optimization have been limited to the proposal of optimization control policies using queue length constraints in oversaturated conditions, and also finding the optimum cycle time and green splits based on either a known cycle time from the field or an optimum cycle time obtained from other methods. To our best knowledge, we can say after reviewing the literature that no attempt has been made to use the shock wave model to find the optimum cycle time for a general isolated intersection, because minimizing this model generates very small values, close to zero for an optimum cycle time, which is unacceptable. In this paper, we propose an optimization model that provides the optimum cycle time and green splits when the total average delay at a general isolated signalized intersection is minimized for all vehicles present. To do so, we model the lost time effect in the shock wave delay model, which creates the most desirable optimum cycle time values. In our optimization process, the key strategy is to keep both approaches in the undersaturated condition. Therefore, our model works when the total amount of volume-to-capacity ratio of both approaches is less than 2.0; otherwise, where both approaches are oversaturated, other control policies should be considered and utilized. A comparison of the results with a widely-used formula in the literature reveals that our model is superior.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".