<i>Highway Capacity Manual</i> and Highway Capacity Software 2000 and Advanced Transportation Modeling Tools: Focus Group Findings
Bibliographic record
Abstract
Focus groups were conducted in several cities throughout the United States and Canada between June 2002 and July 2003 to gain insight into the use of the newly released Highway Capacity Manual (HCM) 2000 and to understand better how professionals choose advanced modeling tools. Part I of the HCM 2000 was noted as being a good reference for both laypersons and those working with the HCM on a daily basis. Part V was noted as needing improvement to be useful to professionals, including more specific information regarding model strengths and weaknesses. In regard to the use of computer-based models, more than 80% of the participants use the HCM and the Highway Capacity Software to analyze operational performance and estimate capacity of highway systems. For advanced modeling techniques, 70% of those surveyed turn to SYNCHRO, but few participants could say why they had chosen a particular software package other than being directed by a sponsor agency to use it. It is noted that focus groups are a useful means to obtain qualitative and quantitative information regarding the use of the HCM 2000 and to identify areas of the manual that need improvement and further research. In addition, the information gleaned from this study points to the need for guidance regarding the selection and application of advanced transportation modeling tools. Finally, it is noted that additional focus groups should be held to include opinions from other user groups including planners, decision makers, and designers.
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 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 | 0.023 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".