Analysis, Modeling, and Simulation (AMS) Case Studies of Connected and Automated Vehicle (CAV) Implementations Specific to the South Central Region
archive: archived pipeline: cataloged verified
Get this paper ↗ (full text — opens at the source; we link to it, we don't host it)
Summary
This report documents preparatory efforts for Connected and Automated Vehicle (CAV) technologies in Louisiana, conducted by the Transportation Consortium of South-Central States (Tran-SET) and Louisiana State University. The study addresses the challenge of agency readiness for CAV deployment, noting that realized benefits depend heavily on how well public agencies prepare. The research expanded from its original intent of conducting Analysis, Modeling, and Simulation (AMS) case studies to include stakeholder engagement and crash analysis, aligning with the Louisiana Department of Transportation and Development’s (LaDOTD) CAV Technology Team initiatives. The methodology comprised two primary components. First, an electronic survey was disseminated to 273 Louisiana organizations, yielding 117 responses from local, state, federal, nonprofit, and private entities. Responses were clustered into three groups based on awareness and perceived impact: Group A (uninformed, low perceived impact), Group B (informed, low perceived impact), and Group C (aware, positive perception, high importance of preparation). Second, a crash analysis was performed at four proposed locations for Queue Warning Systems (QWSs): segments of I-110 in East Baton Rouge Parish, I-10 in Jefferson Parish, I-10 in West Baton Rouge Parish, and I-12 in St. Tammany Parish. This analysis utilized five-year historical crash data, evaluating crash rates, severity, collision manner, and Level of Service of Safety. Key findings revealed a statistically significant correlation between awareness and positive perception of CAV technologies. However, economic development, freight, and transit groups exhibited low awareness and perceived impact, identifying them as areas of concern. Only 22.2% of responding organizations were currently preparing for CAVs, with federal agencies showing the highest readiness (100%) and nonprofits the lowest (8.3%). Regarding the crash analysis, QWSs were deemed suitable for deployment at the Jefferson Parish and West Baton Rouge Parish locations due to an overrepresentation of rear-end crashes. Conversely, existing modeling networks were found unsuitable for AMS case studies due to limited geospatial coverage, though an independent microsimulation model of I-10 in Baton Rouge was identified as a potential candidate for future mobility-based CAV application testing. The study concludes with recommendations for LaDOTD to foster strategic partnerships, including establishing an external CAV advisory council, creating targeted outreach plans with educational components, conducting knowledge and skills gap analyses, and executing pilot demonstrations. These actions aim to support agencies in the "directed" or lower maturity categories of CAV planning, providing a framework for policy, planning, and integration strategies applicable to similar transportation agencies lacking direct deployment experience.
Key finding
Survey results revealed a strong correlation between CAV awareness and positive perception, while crash analysis indicated that Queue Warning Systems are suitable for deployment at Jefferson Parish and West Baton Rouge Parish locations due to high rates of rear-end collisions.
Methodology
mixed_methods
Sample size: 117
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 24 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Empirical Findings: crash risk outcomes
- Methodological Resource: dataset resource