Bibliographic record
Abstract
The flow on urban and suburban freeways is characterized typically by high densities, occupancies, and frequent breakdowns, particularly when the volume approaches capacity. This creates unstable flow conditions, resulting in recurrent as well as random congestion. The purpose of this study was twofold: (1) to develop models for evaluating flow characteristics on a busy suburban freeway, specifically speed versus flow and flow versus occupancy; (2) to understand the meaning of capacity by evaluating the actual capacity of a freeway section. The data were collected on Highway 1 in Israel and on Interstate 66 in Virginia, USA. Previous observations showed that both freeways operated at a saturated-flow condition almost daily and that breakdowns of the stream occurred often, though not daily. The paper presents a concept that suggests that freeway capacity is an actual-site specific term. This capacity can change frequently in space and time, depending on the section geometry, the time interval, the prevailing traffic and environmental conditions, and driving behavior. Determination of a representative value of actual capacity for level-of-service or planning analyses is suggested, based on one of three methods: parabolic curve-fitting for free-flow, dense congested stable (DCS) flow, and breakdown-flow data; the intersection of the best-fit stable-flow parabola and the equal-density adjusted line; and curve fitting of one-regime models to flow-occupancy data. Also included is a comparison with the 2000 Highway capacity manual (HCM) models and with capacity estimates from other studies. The results showed similar values of actual capacity estimates for the three methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".