Exploring suitable traffic conditions for intermittent bus lanes
Bibliographic record
Abstract
Summary As one of the most promising bus priority techniques, the innovative intermittent bus lane (IBL) strategy has drawn more attention in the past few years. In this paper, some improvements on the operation of the IBL strategy are proposed, and two cellular automaton models for a roadway section with two lanes, one with no bus priority and the other with an intermittent bus lane, are built to study the characteristics of urban traffic flow. Computer simulations and analytical models are developed to conduct quantitative research on the influence of IBL on the traffic density distribution, traffic velocity, and traffic capacity of the roadway section. By comparing the average paces in the two cases, this paper proposes a methodology to determine suitable traffic conditions for the IBL strategy implementation. The results indicate that for the designed scenarios, the IBL strategy is effective only when the traffic density is in the range of 25 to 74 pcu/km, which suggests that level of service C is the inflection point for implementing the IBL strategy. Copyright © 2014 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".