Bibliographic record
Abstract
The merits of separating cars and trucks have long been debated. Potential advantages include smoother traffic flows, lower accident rates, improved air quality and reduced maintenance and road infrastructure costs. Large trucks are often banned from urban roads and restricted to certain lanes on many highways but there are no dedicated truck facilities. However, truck-only lanes and truck tollways are now being actively studied. Tolls on cars and trucks are also becoming increasingly common and could be used to distribute car and truck traffic over road networks more efficiently. This paper reviews the potential benefits from separating cars and trucks onto different lanes or roads while treating road infrastructure as given. U.S. studies of mixed traffic operations, lane restrictions and differential speed limits do not provide consistent evidence whether separating cars and trucks either facilitates traffic flows or reduces accident rates. Analysis with an economic model reveals that the potential benefits depend on the relative volumes of cars and trucks, capacity indivisibilities and the impedance and safety hazard that each vehicle type imposes. Differentiated tolls can support efficient allocations of cars and trucks between lanes. Lane access restrictions are much more limited in effectiveness. Toll lanes that are dedicated to either cars or trucks are a potentially attractive hybrid policy. Intelligent Transportation Systems (ITS) technology can help to improve safety and travel time reliability, and help drivers select between tolled and untolled routes.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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; both teacher heads agree on what is shown here.
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".