Managing a Sustainable Transportation System: Exploring a Community’s Attitude, Perception, and Behavior of the Morgantown Public Rapid Transit (PRT)
Bibliographic record
Abstract
Automated transportation is an innovative and sustainable concept that works emission-free with fully-automatedand driverless vehicles on a network of specially-built, elevated guide ways. These systems are also calledAutomated People Mover (APM) or Public Rapid Transit (PRT) and are considered to be a solution to many globaland environmental problems related to the use of the automobile. These transportation systems claim to be clean,affordable and safe technology, and a smart urban planning solution to move away from America’s dependence onforeign oil, the faltering auto industry, and the misuse of urban landscapes. One of the first APM systems has beenoperating since the 1970’s at West Virginia University in Morgantown, West Virginia. In order to examinecommunity’s attitude, perception and individual characteristic that influence the use of the systems, a randomintercept survey was conducted. Findings from correlation analysis and an ordered probit model suggestsocio-demographic attributes associated with attitudes toward the system. The frequent users are characterized ashaving a higher level of educational attainment, and are primarily students. Findings explore underlying factorsregarding commuting, crucial for transportation policies and practices for managing sustainable transportationsystems in comparable urban settings.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".