An investigation of multiplier effects generated by implementing queue jump lanes at multiple intersections
Bibliographic record
Abstract
Summary This paper investigates the combination effects of queue jump lanes (QJLs) on signalised arterials to establish if a multiplier effect exists, that is, the benefit from providing QJLs at multiple intersections is higher than the sum of benefits from providing them individually at each of those intersections. To explore the combination effects on bus delay and total person delay, a delay estimation model is developed using kinematic wave theory, kinematic equations and Monte Carlo simulation. In addition, to investigate the combination effects in offset settings optimised for bus delay or total person delay, offset optimisation models are proposed. Validation results using traffic micro‐simulation indicate the effectiveness and computational efficiency of the proposed models. Results of a modelling test bed suggest that providing QJLs at multiple intersections can create a multiplier effect on one‐directional bus delay savings with signal offsets that provide bus progression. Furthermore, optimising offsets to minimise bus delay tends to create a multiplier effect on one‐directional bus delay savings, particularly when variations in dwell times are not high. The reason for the multiplier effect may be that providing QJLs reduces variations in bus travel times, which makes signal coordination for buses perform more effectively. From a policy perspective, the existence of a multiplier effect suggests that a corridor‐wide scale implementation of QJLs has considerable merit. Copyright © 2016 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".