Connected Vehicle Pilot Deployment Program Performance Measurement and Evaluation– Tampa (THEA) CV Pilot Phase 3 Evaluation Report
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Summary
This report evaluates the performance of the Tampa Hillsborough Expressway Authority (THEA) Connected Vehicle (CV) Pilot Deployment, specifically focusing on Phase 3. The study addresses the need to measure the impact of CV technology on mobility and safety in downtown Tampa, Florida. It assesses six specific use cases: morning backups, wrong-way entries, pedestrian conflicts, transit signal priority, streetcar conflicts, and traffic progression. The evaluation aims to determine whether vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) applications effectively enhance traveler and pedestrian safety while improving traffic flow. The research employed a robust experimental design involving more than 1,000 private citizens who installed On-Board Units (OBUs) with Human-Machine Interfaces (HMIs) in their vehicles. Participants were randomized into treatment (HMI enabled) and control (HMI disabled) groups using a two-to-one matching ratio based on socio-demographics. The study utilized a before-after assessment and interrupted time-series analysis to evaluate behavioral responses to CV applications. Data collection included CV data, participant surveys, and non-CV data sources. The methodology analyzed application performance by estimating false positive, false negative, true positive, and true negative rates for each use case. Specific applications evaluated included Electronic Emergency Brake Light, Forward Collision Warning, Intersection Movement Assist, Vehicle Turning Right in Front of Transit Vehicle, End of Ramp Deceleration Warning, Pedestrian Collision Warning, Transit Signal Priority, and Wrong-Way Entry detection. The findings provide evidence that the CV deployment contributed to enhancing both mobility and safety for travelers and pedestrians. However, the analysis revealed heterogeneity in how V2V and V2I applications performed across the six use cases. For instance, the End of Ramp Deceleration Warning showed measurable impacts on travel time, travel time reliability, idle time, and queue length. Safety analyses for applications like Forward Collision Warning and Pedestrian Collision Warning detailed specific rates of true and false positives, highlighting variations in detection accuracy and warning visibility among participant groups. Participant surveys indicated varying levels of satisfaction and perception of CV technologies, with data showing differences in experience between treatment and control groups. The report also documents specific crash rates and conflict rates associated with certain use cases, such as streetcar conflicts and traffic progression, providing a granular view of system effectiveness. The significance of this study lies in its detailed analytical approach to evaluating CV use cases, offering insights into the real-world performance of connected vehicle technologies. The report identifies limitations related to performance evaluation goals, study area specifics, and technology readiness. It provides lessons learned from the deployment, which are critical for future CV implementations. By quantifying the impact of CV applications on mobility and safety metrics, the study supports the broader understanding of how connected vehicle technologies can be integrated into intelligent transportation systems to improve urban traffic conditions. The findings underscore the importance of tailored evaluation methods for different use cases and highlight the variability in application performance, informing future development and deployment strategies.
Key finding
The deployment of connected vehicle technologies contributed to enhancing the mobility and safety of travelers and pedestrians, with performance varying across the six evaluated use cases.
Methodology
field_study
Sample size: 1000
Provenance
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| 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 |
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| 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.
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Information type
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- Applied Guidance: countermeasure evaluation
- Empirical Findings: crash risk outcomes
- Methodological Resource: validation psychometrics