Social Impact Analysis of Various Road Capacity Expansion Options: A Case of Managed Highway Lanes
Bibliographic record
Abstract
Managed lanes (MLs) offer public infrastructure owners a key policy lever for reducing the financial burden of road expansion while managing induced travel demand. ML’s impact varies depending on the operational method adopted. Previous literature has focused on only one option’s optimum toll estimation, operational strategies, or impacts at a time. In this study, we provide the first detailed network-wide comparisons of MLs that include travel time, vehicle-miles-traveled (VMT), general-travel cost, fuel consumption, and emissions. We embed a toll-choice model within a four-step travel demand model considering drivers’ value of travel time (VOTT). The study uses existing high-occupancy-toll (HOT) lanes and the surrounding network in the Dallas-Fort-Worth, Texas, area as a case-study area. We find the following: (1) HOT lanes are the preferred option providing the highest travel-time savings; (2) the all-tolled option performs the best at the corridor level. It reduces corridor travel time by around 20%. However, lower traffic volume on tolled lanes generates lower overall network performance; (3) high-occupancy-vehicle (HOT) performs the worst and is similar to the do nothing option; and (4) both priced options, all-tolled and HOT, generate the smallest total emissions and fuel consumption.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".