A new lost time estimation method for right‐turn traffic in Japan considering signal phasing and sneakers
Bibliographic record
Abstract
SUMMARY Precise estimation of the capacity for right‐turn traffic (comparable to left‐turn traffic in the USA) is of great importance to determine signal phasing schemes at signalized intersections in Japan, where the left‐hand driving rule is valid. However, in most signal timing procedures across the world, the lost time of right‐turn traffic is simply determined by the duration of intergreen intervals and thus lacks considerations of various signal phasing and driver behavior. Meanwhile, sneakers per cycle are usually applied to account for the number of drivers completing right turns during the effective red portion of the clearance‐and‐change intervals. As a result, an initial cycle length must be hypothesized in order to assess the total number of sneakers within the analysis period. Consequently, a time‐consuming iterative calculation process often becomes necessary. Therefore, the present study aims to develop a new lost time estimation method for right‐turn traffic to overcome the aforementioned drawbacks. Lost times of right‐turn traffic under three conventional phasing plans are theoretically formulated on the basis of a time–space diagram and shock‐wave theory. The new method is validated using field data, with case studies of its application in the signal timing procedure. Results indicated that the proposed method is capable of offering more accurate estimation than conventional approaches, which leads to shorter cycle length and simplifies signal timing process by eliminating an iterative check to determine the number of sneakers. Copyright © 2012 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 | 0.001 |
| 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".