A review on state-of-the-art practices and research of using GIS in transportation corridor planning
Bibliographic record
Abstract
Transportation corridor planning is a process that is in nature collaborative with local governments and includes extensive public participation opportunities. A corridor may be divided into logical, manageable smaller areas for the purpose of corridor planning. The planning process looks at the existing transportation system within the corridor and how the system could be changed or expanded to meet long-term needs, and includes discussion of existing and projected travel patterns and social, environmental, and economic issues within the corridor. It includes discussion of infrastructure improvements in combination with wise land-use and systems-management actions. GIS is assessed as [an] advanced tool because of the spatial nature of transportation planning and the determination of a range of potential outcomes. The research is intended to investigate the state-of-the-art technology with a goal of greatly improving [the] corridor planning process together with understanding of GIS capabilities, data awareness and accuracy, decision-making and communications. GIS is utilized as a tool in such a way to enhance the ability to accurately predict and easily understand these capabilities. Its main motivation is to better represent GIS in the corridor planning process. It is intended to provide transportation organizations, planning practitioners, and transportation decision-makers with GIS tools and guidance for planning, organizing, and managing to effectively support transportation investment decisions tailored to the specific conditions and performance needs for major transportation improvements. This research proposes to address the capabilities of GIS in corridor planning and enhance the ability to accurately predict and easily understand these capabilities.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".