Social Impact Analysis of Various Road Capacity Expansion Options: A Case of Managed Highway Lanes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it