Safety Impact Assessment of New York City Connected Vehicle Pilot Safety Applications

Lam, Andy; Chupp, William; Najm, Wassim G; Brewer, John; Fallings, Deirdre · 2022 · ROSA P / United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office

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Summary

This report presents the safety impact assessment of nine connected vehicle (CV) safety applications deployed in New York City’s Connected Vehicle Pilot (CVP) site. The study evaluates the effectiveness of five vehicle-to-vehicle (V2V) applications—forward collision warning, emergency electronic brake light, lane change warning, blind spot warning, and intersection movement assist—and four vehicle-to-infrastructure (V2I) applications—posted speed compliance, curve speed compliance, speed in work zone compliance, and red light violation warning. The research aims to determine whether these automated alerts influence driver behavior and reduce safety risks in real-world urban driving conditions. The assessment utilized naturalistic driving data collected from 3,000 vehicles equipped with aftermarket safety devices between January and December 2021. The experimental design compared a treatment group, which received active alerts, against a control group that received silent alerts. The dataset comprised 160,289 total alert events, with 67% generated by V2I applications and 33% by V2V applications. Of these, 59% were active alerts and 41% were silent. The analysis involved rigorous data filtering to remove invalid events and statistical testing to identify significant differences in vehicle and driver responses between active and silent alert conditions. Key metrics included speed compliance, red light violation rates, rear-end near-crash rates, and lane change behaviors. The findings indicate that active alerts significantly improved safety outcomes across multiple categories. For V2I applications, active alerts led to decreased speed non-compliance and reduced red light violation rates. Specifically, drivers receiving active warnings for posted speed, curve speed, and work zone speed compliance demonstrated better adherence to speed limits compared to those receiving silent alerts. Similarly, active red light violation warnings resulted in fewer violations at intersections. For V2V applications, active alerts correlated with a reduction in rear-end near-crash rates and unsafe lane change rates. Drivers responded to forward collision warnings and electronic emergency brake lights with increased deceleration and shorter brake reaction times. Lane change and blind spot warnings also resulted in fewer unsafe lateral maneuvers. Statistical tests confirmed that these behavioral changes were significant when comparing active alert responses to silent alert baselines. The study concludes that connected vehicle safety applications provide measurable safety benefits by modifying driver behavior in real-time. The deployment of V2V and V2I technologies in a dense urban environment like New York City demonstrates that automated warnings can effectively mitigate risks associated with speeding, intersection violations, and collision scenarios. These results support the broader integration of connected vehicle technologies into intelligent transportation systems, suggesting that such applications can enhance road safety by providing timely information that prompts safer driving decisions. The report provides empirical evidence for the efficacy of specific CV applications, offering insights for future deployment strategies and policy development in intelligent transportation systems.

Key finding

Response to active connected vehicle alerts resulted in statistically significant decreases in speed non-compliance, red light violation rates, rear-end near-crash rates, lane change rates, and unsafe lane change rates.

Methodology

naturalistic

Sample size: 3000

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).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• naturalistic crash near crash

Information type

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• Applied Guidance: countermeasure evaluation
• Empirical Findings: crash risk outcomes, observational prevalence