Investigating Automated Shuttle Readiness for Rural Areas: North Carolina Case Study
Bibliographic record
Abstract
The past two decades have seen the potential of automated shuttles in addressing transportation needs for vulnerable communities; however, the readiness of rural communities for the deployment of these shuttles remains unexplored. This research evaluates the level of readiness of rural counties in North Carolina for implementing autonomous vehicle (AV) technology, focusing on physical infrastructure, digital infrastructure, and social vulnerability using a range of indicators derived from secondary data sources, including road quality, bridge conditions, broadband availability, and socioeconomic factors. The analysis reveals a diverse landscape of readiness across the counties, with variations in physical and digital infrastructure and social vulnerability indices. Despite being rural and located in coastal regions, Camden County emerged as a leading candidate for AV deployment as a result of the better availability of the internet, good road conditions, and a population in need of autonomous shuttles. This was followed by Halifax, Chatham, and Bladen counties. To further understand regional differences, we used clustering analysis to identify distinct clusters within census tracts that reflect varying readiness levels. While some clusters are well-positioned for early AV deployment, others require targeted interventions to improve infrastructure. Conversely, counties with higher social vulnerabilities indicate a potential need for autonomous shuttle interventions to enhance the livelihood of the population needing these services. The study findings emphasize the importance of a multiweighted analysis for readiness assessments that can guide investments and strategic interventions, and the potential of data-driven approaches that can be generalized to other states for determining readiness.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".