The Highway Capacity Manual Delay Formula for Signalized Intersections BY RAHMI AKCELIK
Bibliographic record
Abstract
he purpose of this article is to compare the 1985 U.S. Highway Capacity Manual (HCM) ’ delay for”mula for signalized intersections with the Australian ’ and Canadian3 formulas and to present a generalized form that embraces them all. The aim is to promote international cooperation in this area of research and development. Compared with the other delay formulas, the HCM formula predicts higher delays for oversaturated conditions, and the differences between the prediction from the HCM formula and the other formulas increase with increasing degree of saturation. An alternative to the HCM delay formula, which matches the other formulas for oversaturated conditions, is given. The alternative formula, derived from the generalized formula, predicts delays that are very close to those predicted by the original HCM formula for undersaturated conditions, and at the same time predicts delays that are very close to the results from the Australian and Canadian formulas for oversaturated conditions. The HCM signalized intersection chapter states that its delay formula “yields reasonable results for values of.x between 0.0 and 1.0... The equation may be used with caution for values of x up to 1.2, but delay estimates for higher values are not recommended. ” Although traffic engineers do not design for oversaturation, a delay formula that can be expected to give reasonable results for oversaturated, as well as undersaturated conditions, is preferred because the limitations of this type of formula are often forgotten and the formula misused in practice (e.g., in evaluating alternative designs or in stating benefits from improvements to an existing oversaturated intersection). IThe HCM formula predicts higher delays for oversaturted conditions. The generalized formula could be calibrated to develop a more suitable formula for U.S. conditions. It might also be possible to calibrate it for vehicle-actuated and fixed-time signals separately. Various other issues related to this discussion and briefly mentioned in this article will be discussed elsewhere.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".