Effects of Implementing Night Operation Signal Coordination on Arterials
Bibliographic record
Abstract
Traffic signal coordination, which connects a series of signals along an arterial by various coordination methodologies, has been proven as one of the most cost-effective means for alleviating traffic congestion. Various metropolitan planning organizations (MPO) or transportation management centers (TMC) have included signal timing updates in their strategic plans. However, in practice, signal coordination is usually implemented when traffic volume is heavy (i.e., during peak hours). For the rest of the day, the free operation strategy is usually used to reduce the waiting time of uncoordinated phases. However, this free operation strategy may result in the loss of operational efficiency on the major street. Currently, implementing signal coordination during off-peak hours is rare in the U.S. since there is lack of an efficient method that considers traffic operations for both the major and the minor streets. Therefore, this research provides a novel method that balances the control delays between the major street and the minor street. The procedure is to optimize the splits of the major street while also using the reservice strategy in the signal controller for the minor street. Microsimulation modeling was employed to assess the performance of traffic signal coordination during off-peak periods. Results show that, under reasonable splits, the coordination effect on the major street can be achieved and protected with an acceptable delay to minor street traffic. The strategy can be immediately implemented to reduce travel time for major street traffic.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".