Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) Passenger and Freight Vehicle Applications to Enhance Safety and Efficiency in Coastal Evacuations
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 study investigates driver response and acceptance to Connected and Automated Vehicle (CAV) technologies, specifically Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications, during hurricane evacuations. While prior research utilized microsimulation to assess network-wide performance, it largely ignored individual driver behavioral factors. This research addresses that gap by examining how drivers interact with V2V and V2I advisories and how these interactions impact traffic safety and efficiency in emergency scenarios. The researchers conducted a driving simulator experiment using a high-fidelity Ford Fusion simulator at Louisiana State University. The simulated environment replicated a 5.8-mile segment of Louisiana I-10 East, designed to mimic a coastal evacuation route. Seventy-nine licensed drivers participated after completing a warm-up session; participants were randomly assigned to drive a base scenario with no warnings and a scenario featuring specific V2I and V2V warnings. The warnings included V2I alerts for rain, congestion, and alternate routes, as well as a V2V alert for potential rear-end collisions. These advisories were delivered via in-vehicle displays and audio messages. Data on speed, time-to-collision (TTC), and compliance were collected and analyzed using mixed linear models to account for fixed factors like gender, age, and experience, alongside the presence of warnings. The results demonstrated high compliance with safety-critical warnings. Approximately 90% of drivers complied with V2I rain warnings and V2V crash warnings, while 89.9% followed V2I alternate route information. In contrast, compliance with V2I congestion warnings was low, with less than 50% of drivers reducing speed. Behaviorally, rain warnings significantly reduced average speeds during precipitation (45.14 mph with warning vs. 55.62 mph without). V2V crash warnings resulted in higher minimum TTC values (17.56 seconds vs. 15.26 seconds), indicating improved safety margins. Similarly, congestion warnings led to higher TTC values, though the difference was not statistically significant. Notably, 74 out of 79 drivers took the suggested alternate route when advised via V2I, compared to only 23 drivers who did so when relying solely on dynamic message signs. Post-experiment surveys indicated that over 80% of participants found the V2I and V2V messages extremely useful or useful. The study concludes that V2I and V2V technologies can significantly enhance safety and efficiency during coastal evacuations by influencing driver behavior. High compliance with rain, crash, and routing advisories suggests that connected vehicle systems can reduce speeds in adverse weather, increase safety margins during congestion, and effectively distribute traffic across alternate routes. These findings provide critical insights for designing and implementing connected vehicle technologies to improve transportation system resilience during critical events like hurricanes.
Key finding
Drivers demonstrated high compliance with V2I rain warnings, V2V crash warnings, and alternate route information, resulting in improved safety metrics such as reduced speeds and increased time-to-collision during simulated hurricane evacuations.
Methodology
simulator
Sample size: 79
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).
- Applied Guidance: countermeasure evaluation