Utilization of Connected Vehicle Data to Support Traffic Management Decisions
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Summary
This report investigates the utilization of connected vehicle (CV) data to support traffic management operations within the Florida Department of Transportation’s (FDOT) SunGuide system. Motivated by the rapid advancement of vehicle-to-infrastructure (V2I) technologies, the study aims to prepare FDOT for next-generation traffic management strategies. The research assesses the quality and applicability of CV data for nine specific SunGuide modules, including incident detection, travel time estimation, ramp metering, and queue warning systems. The project combines literature reviews, data analysis from existing deployments, microscopic simulation, and a field demonstration to determine when CV data can supplement or replace traditional data acquisition systems. The methodology involved a comprehensive assessment of CV data elements and communication technologies, concluding that both Dedicated Short Range Communications (DSRC) and cellular communications support dynamic mobility applications. Researchers analyzed archived CV data from the Orlando deployment and the Ann Arbor Safety Pilot, noting the necessity of data preprocessing due to erroneous inputs. A field test was conducted by installing Onboard Units (OBUs) and cellular modems on FDOT District 5 Road Ranger vehicles to collect real-time data along Interstate 4 in Central Florida. Additionally, the study developed methodologies to estimate link-level variations in CV market penetration based on socioeconomic characteristics and used simulation modeling combined with the Surrogate Safety Assessment Model (SSAM) to evaluate safety benefits. Regression models were also developed to assess the accuracy of travel time estimation and incident detection under varying traffic demand levels and CV proportions. Key findings indicate that CV data can effectively supplement point traffic detectors and verify their accuracy. The study found that CV market penetration varies significantly by region and facility type, following a lognormal distribution, with the highest variation occurring in the first year of implementation. For travel time estimation, CV data quality becomes sufficient for freeway operations in the first year after a mandatory CV installation mandate, whereas urban streets require one to three years for planning and three to six years for real-time operations. Regarding safety, a CV market penetration of only 3% to 6% is sufficient for accurate queue length estimation, outperforming traditional detectors. Queue warning systems using CV data significantly reduce rear-end conflicts when driver compliance exceeds 15%. For traffic volume estimation on urban streets, CV data can improve estimates on undetected links after four years, with full detector removal feasible after 10 to 15 years depending on accuracy thresholds. The significance of this work lies in providing FDOT and other transportation agencies with a data-driven timeline for transitioning from traditional sensors to CV-based data acquisition. The report offers specific recommendations for integrating CV data into existing SunGuide modules and establishes benchmarks for market penetration required to achieve reliable performance in travel time, incident detection, and volume estimation. These findings support strategic investment planning for Transportation System Management and Operations (TSM&O) programs, ensuring agencies are prepared to leverage emerging CV technologies for improved traffic management and safety.
Key finding
A connected vehicle market penetration of 3% to 6% is sufficient for accurate and reliable queue length estimation, while full replacement of existing detectors for traffic volume estimation on urban streets may require 10 to 15 years.
Methodology
mixed_methods
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.
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- Empirical Findings: observational prevalence
- Methodological Resource: dataset resource, validation psychometrics