Freeway Facility Methodology in Highway Capacity Manual 2000
Bibliographic record
Abstract
The next edition of the Highway Capacity Manual (HCM 2000) will contain for the first time an operational analysis procedure for directional freeway facilities up to 20 to 25 km long. At the simplest level, this procedure integrates the proposed HCM 2000 methods for the analysis of basic, ramp, and weaving segments to enable the analysis of an entire facility. But the proposed facility methodology goes much further. It allows the user to analyze multiple, contiguous time intervals with timevarying demands and capacities. It can handle both undersaturated and oversaturated traffic conditions (with some limitations). In the latter case, both the spatial and time extent of congestion are estimated. Finally, the method permits the investigation of the effect of many traditional and intelligent transportation system–based freeway improvement strategies such as full or auxiliary lane additions, ramp metering, incident management, and a limited set of high-occupancy-vehicle designs on facility performance. Described here is the conceptual model for and computational steps of the methodology, with emphasis on the components for analysis of oversaturated conditions. The scope and limitations of the methods are also highlighted. Reference is given to a companion paper that describes how the results of the method were validated in the field and how they compared with those obtained from widely used freeway simulation models.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.064 | 0.022 |
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".