Turn Pocket Blockage and Spillback Models
Bibliographic record
Abstract
The development of a macroscopic simulation tool is presented and tested to quantify the effects of short turn pockets on the sustainable service rate of a signalized intersection. Unlike the theoretical signal capacity, the sustainable service rate includes queue interaction effects and is thus influenced by blockage and spillback at the entrance to a short turn pocket. Previous research on the topic has focused either on the probability of spillback from a short turn pocket or the operation of a system with a single approach lane. No macroscopic model currently available has the ability to analyze throughput reductions due to the effects of short turn pockets on a multilane approach. The model described here uses a series of flow and density restrictions on cells of varying sizes on the approach to the intersection. Results, which indicate sensitivity of the model to turn pocket spillback, blockage, saturation flow rate, pocket length, lane utilization, cycle length, phase sequence, phase overlap, permitted phasing, and time-dependent demand, compare favorably with microsimulation. A phase optimization procedure is described to help efficiently allocate green time for a given set of turn pocket lengths and turn movement percentages. Implications of using the model for signal timing applications are discussed, and recommendations are made on additional model enhancements and testing needs.
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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) | 0.000 | 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) | 0.005 | 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".