Impact of Connected Vehicle Technology on Traffic Safety under Different Highway Geometric Designs

Azin, Bahar; Wang, Qinzheng; Yang, Xianfeng (Terry); Gong, Yaobang · 2021 · ROSA P / Mountain-Plains Consortium

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Summary

This study investigates the impact of Connected and Automated Vehicle (CAV) technology on traffic safety across various highway geometric designs. The research is motivated by the fact that current road geometric standards are based on human driver reactions, which are prone to error and distraction, accounting for approximately 95% of crashes. As CAVs introduce improved driving behaviors through connectivity and automation, the authors aim to determine quantitatively and qualitatively how these vehicles mitigate safety risks associated with specific road layouts, such as curves, grades, and intersections, particularly in mixed traffic environments with Human-Driven Vehicles (HDVs). The methodology employs microsimulation using VISSIM software to model five distinct scenarios based on historical crash data from Salt Lake City, Utah. These scenarios represent high-risk geometric features, including work zones, superelevated signalized intersections, freeways with weather advisories, on/off-ramps, and complex horizontal/vertical alignments. The simulations vary CAV penetration rates to assess their influence on traffic flow. CAV driving behaviors, such as smoother acceleration, deceleration, and cooperative lane changing, were parameterized to reflect advanced driver-assistance systems. Safety performance was evaluated using the Surrogate Safety Assessment Model (SSAM), which analyzes trajectory data to identify potential conflicts, crash severity, frequency, and classification. The results indicate that higher CAV penetration rates generally improve traffic safety performance by reducing deceleration rates and minimizing rear-end and lane-changing conflicts. This improvement is attributed to CAVs’ ability to adjust speeds and execute cooperative maneuvers, leading to lower speed variance and less severe potential crashes. However, the study found that safety performance did not improve at signalized intersections. This lack of benefit is attributed to the complex interactions between CAVs and HDVs in controlled environments, where limited information exchange and mixed driving behaviors may negate the advantages of CAV technology. Statistical tests confirmed significant safety improvements in most scenarios, except for the signalized intersection case. The significance of this research lies in its demonstration that CAVs can effectively enhance safety on roadways with challenging geometric designs, particularly freeways and areas with limited sight distance, by reducing human error and smoothing traffic flow. However, the findings highlight that CAV benefits are not universal; mixed traffic interactions at signalized intersections may not yield immediate safety gains. This suggests that while CAVs offer substantial potential for reducing crash severity and frequency, infrastructure and traffic management strategies must account for the limitations of mixed-traffic environments. The study provides a quantitative basis for understanding how CAV adoption interacts with existing road designs, informing future transportation planning and safety assessments.

Key finding

Higher CAV penetration rates improve traffic safety by reducing conflicts and severity in most geometric scenarios, but signalized intersections do not show safety improvements due to CAV-human vehicle interactions.

Methodology

simulator

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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• v2x connected vehicle
• perceptual countermeasures

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).

• Empirical Findings: crash risk outcomes
• Methodological Resource: dataset resource