Optimal Corridor Selection for a Road Space Management Strategy: Methodology and Tool
Bibliographic record
Abstract
Nationwide, there is a growing realization that there are valuable benefits to using the existing roadway facilities to their full potential rather than expanding capacity in a traditional way. Currently, state DOTs are looking for cost-effective transportation solutions to mitigate the growing congestion and increasing funding gaps. Innovative road space management strategies like narrowing of multiple lanes (three or more) and shoulder width to add a lane enhance the utilization while eliminating the costs associated with constructing new lanes. Although this strategy (among many) generally leads to better mobility, identifying optimal corridors is a challenge and may affect the benefits. Further, there is a likelihood that added capacity may provide localized benefits, at the expense of system level performance measures (travel time and crashes) because of the relocation of traffic operational bottlenecks. This paper develops a novel transportation programming and investment decision method to identify optimal corridors for adding capacity in the network by leveraging lane widths. The methodology explicitly takes into consideration the system level benefits and safety. The programming compares two conflicting objectives of system travel time and safety benefits to find an optimal solution.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".