Safety Impact Assessment of THEA Connected Vehicle Pilot Safety Applications
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Summary
This report presents the safety impact assessment of seven connected vehicle (CV) safety applications deployed during the Tampa-Hillsborough Expressway Authority (THEA) Connected Vehicle Pilot (CVP) program. Conducted by the Volpe National Transportation Systems Center for the U.S. Department of Transportation, the study aimed to evaluate the crash avoidance effectiveness and driver performance changes associated with vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) technologies. The pilot, which ran from March 2019 to June 2020, involved approximately 800 participant vehicles equipped with aftermarket CV devices in downtown Tampa. The seven applications assessed included Forward Crash Warning (FCW), Emergency Electronic Brake Light (EEBL), Intersection Movement Assist (IMA), Vehicle Turning Right in Front of Transit Vehicle (VTRFTV), Pedestrian Collision Warning (PCW), End of Ramp Deceleration Warning (ERDW), and Wrong-Way Entry (WWE). The methodology utilized naturalistic driving data to compare driver responses between "silent" modes (alerts active in background but not displayed) and "active" modes (visual alerts displayed). The analysis process involved validating alert accuracy, identifying hazardous scenarios, and matching silent and active alerts based on similar initial kinematic conditions, such as speed and time-to-collision. Statistical analyses were then performed to determine if active alerts significantly altered driver behavior, such as braking response or deceleration, compared to silent alerts. Additionally, exposure analysis measured the frequency of vehicle-vehicle and vehicle-infrastructure interactions. The findings revealed limited evidence of safety impact for most applications. For FCW and ERDW, which had sufficient data for statistical comparison, there were no statistically significant differences in driver response metrics between silent and active alert groups, resulting in a crash prevention ratio of one (no effect). For EEBL, IMA, VTRFTV, and PCW, sample sizes were too small or data insufficient to draw statistical conclusions regarding driver response or crash avoidance. The WWE application generated a high volume of alerts, but 94% were filtered out as invalid due to GPS inaccuracies; manual review of the remaining alerts showed no evidence of drivers altering their maneuvers. Exposure analysis indicated that over half of the equipped vehicles communicated with at least one other vehicle, averaging 86.6 minutes of communication time per vehicle during the deployment. The study concludes that while the CV applications were successfully deployed and generated alerts, the specific safety applications evaluated in this pilot did not demonstrate measurable improvements in driver response or crash avoidance effectiveness under the tested conditions. The results highlight challenges in alert validity, particularly regarding GPS accuracy for infrastructure-based warnings, and the difficulty in obtaining sufficient matched data for rare event types like intersection conflicts. These findings provide critical insights for refining CV application performance and experimental designs for future safety impact assessments.
Key finding
The assessment found no statistically significant differences in driver response metrics between silent and active alert groups, resulting in a crash prevention ratio of one for the applications where sufficient data was available.
Methodology
naturalistic
Sample size: 800
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.
- naturalistic crash near crash
- in vehicle coaching
- auditory warnings
- v2x connected vehicle
- adas effectiveness
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
- Empirical Findings: crash risk outcomes
- Methodological Resource: dataset resource