Adaptação do HCM2000 para análise da capacidade e do nível de serviço em rodovias de pista simples no Brasil
Bibliographic record
Abstract
A meta deste trabalho foi descrever, de maneira prática, como aplicar uma adaptação do método do HCM2000 para análise da capacidade e do nível de serviço em rodovias de pista simples brasileiras. A adaptação proposta resulta de duas pesquisas que, basicamente, replicaram os procedimentos usados no desenvolvimento do método original do HCM, substituindo os dados obtidos em rodovias dos EUA e Canadá por dados coletados em dez trechos de rodovias brasileiras. Dessa forma, são apresentadas relações entre a taxa de fluxo, a velocidade média de viagem e a porcentagem de tempo seguindo em pelotões, além de tabelas com fatores de ajuste para condições não básicas. Podem ser avaliados segmentos longos e homogêneos, numa análise em sentido duplo ou em senti-do único, além de rampas específicas. Os resultados obtidos indicam que as estimativas dos níveis de serviço usando-se a adaptação do método do HCM2000 tendem a refletir melhor as reais condições operacionais das vi-as brasileiras de pista simples. ABSTRACTThe goal of this study was to describe, in a practical way, how to apply an adaptation of HCM2000 method for capacity and level of service analysis on two-lane rural highways. The proposed adaptation is the result of two research projects that basically replicated the procedures used for developing the original HCM method, while replacing the data collected on highways in the U.S. and Canada for data from ten sections of Brazilian high-ways. Thus, relationships between flow rate, average travel speed and percent time-spent-following are present-ed, as well as tables containing adjustment factors for situations differing from base conditions. It is possible to estimate the level of service of long homogeneous segments, considering one-way or two-way flows, as well as specific upgrades. The results indicate that the level of service estimates obtained with the use of the HCM2000 adaptation tend to reflect better the actual highway conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".