Connected Vehicle Applications to Improve Infrastructure Safety and Durability
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, produced by the Transportation Infrastructure Durability Center at the University of Maine, examines the applications, regulatory landscape, and deployment challenges of Connected and Autonomous Vehicles (CAVs) with a specific focus on improving infrastructure safety and durability. The research is motivated by the delayed timeline for widespread CAV adoption, which is hindered by difficulties in handling "edge cases," rural road conditions, and seasonal weather phenomena like snow and ice prevalent in northern states. The study aims to clarify how CAV technologies can enhance transportation systems despite these infrastructural and environmental hurdles. The methodology involves a comprehensive review of existing literature, federal and state regulations, and numerous case studies of pilot deployments. The authors analyze data from USDOT programs, such as the Connected Vehicle Pilot Program in Tampa, New York City, and Wyoming, as well as state-led initiatives in New England and rural areas like Iowa and Yellowstone National Park. The report categorizes CAV applications into four primary domains: safety, environment, mobility/traffic management, and durability. It also evaluates the regulatory framework, including NHTSA guidelines and the AV TEST initiative, and assesses technical standards such as the transition from Dedicated Short-Range Communication (DSRC) to Cellular Vehicle-to-Everything (C-V2X). Key findings indicate that while full automation (SAE Levels 4–5) remains distant, connected vehicle technologies offer immediate benefits. Safety applications, such as Curve Speed Warning and Spot Weather Impact Warning, address roadway departures and hazardous conditions, potentially reducing crash costs by billions annually. Environmental applications, including Eco-Approach and Eco-Signal Timing, demonstrate fuel reduction benefits ranging from 5% to 13% by optimizing traffic flow and reducing idling. Mobility applications like Multimodal Intelligent Traffic Signal Systems have shown travel time improvements of 6–27% in field tests. Regarding durability, the report highlights that CAVs can extend infrastructure life by reducing accident-related damage and enabling better maintenance decision support through real-time data on road and bridge conditions. However, challenges persist, particularly in rural areas with limited infrastructure and in regions requiring robust solutions for winter weather. The significance of this work lies in its detailed mapping of the current CAV ecosystem and its implications for infrastructure planning. The report concludes that successful CAV integration requires coordinated federal and state regulatory efforts, standardized infrastructure, and significant investment in communication networks. It emphasizes that while CAVs promise safer and more efficient transportation, their deployment must account for the unique challenges of rural and northern environments. The findings provide a roadmap for policymakers and transportation agencies to prioritize investments in connectivity and infrastructure upgrades that support both connected and autonomous vehicle technologies.
Key finding
Connected and autonomous vehicle technologies can significantly improve infrastructure safety and durability by reducing crash-related property damage and enhancing road weather management, though widespread deployment is delayed by rural infrastructure limitations and regulatory fragmentation.
Methodology
review
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 | — | — | 19 | 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).
- Methodological Resource: dataset resource