Impacts of Low-Speed Vehicles on Transportation Infrastructure and Safety: Final Report
Bibliographic record
Abstract
There are increasing numbers of low-speed electric vehicles (LSVs) on public roadways. These vehicles are designed to be used within protected environments and on roadways with a maximum posted speed of 25 mph. Currently these vehicles are not subject to the same federal requirements for occupant protection as passenger cars. This research project investigated safety standards, operating regulations, and LSV manufacturer materials from sources around the world. The purpose of the research was to determine positive and negative impacts that LSVs, including Neighborhood Electric Vehicles (NEVs) and Medium Speed Electric Vehicles (MSEVs), are likely to have for the Oregon Department of Transportation (ODOT) and Oregon communities, and whether adjustments in current state regulations are needed to ensure that LSVs do not negatively impact road safety and traffic operations, or expose the LSV operators to undue risk. The U.S. and Canadian federal motor vehicle safety agencies have harmonized their regulations and stipulated the maximum operating speed of these vehicles, however state and local roadway authorities have regulated the maximum speed of roadways and intersection characteristics on which these vehicles can operate. The significant recommendations of this research are: (i)The State of Oregon regulations for LSVs should be amended such that LSVs are limited to public roadways with a maximum operating speed of 25 mph, and they are restricted to crossing higher speed roadways at four-way Stop or traffic controlled intersections, (ii) local transportation authorities should develop parallel or secondary low-speed transportation networks that connect residential neighborhoods with major activity centers
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".