Impact of Connected and Autonomous Vehicles on Nontraditional Intersection Design: Superstreets
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Summary
This study investigates the operational impact of Connected and Autonomous Vehicles (CAVs) on superstreets, a nontraditional intersection design that separates turning movements from through traffic to improve safety and capacity. While CAVs have been extensively studied in conventional intersections, freeways, and roundabouts, their effects on innovative intersection geometries like superstreets remain underexplored. The research aims to determine the market penetration rates at which CAVs yield operational benefits, quantify the extent of these benefits, and analyze how performance varies across different traffic scales and CAV capabilities, specifically platooning and trajectory planning. The researchers employed a simulation-based experimental design using the Simulation of Urban MObility (SUMO) platform. A real-world superstreet located at U.S. 17 and Walmart/Gregory Road in Leland, North Carolina, was replicated using historical traffic volume data. Human-driven vehicles were modeled using the Wiedemann 99 car-following model, with parameters calibrated via a genetic algorithm to match observed average speeds. CAVs were modeled using the Intelligent Driver Model (IDM) and assumed to possess vehicle-to-infrastructure communication capabilities, allowing them to adjust trajectories based on Signal Phasing and Timing (SPaT) information to minimize fuel consumption. The study also incorporated CAV platooning behaviors and evaluated performance under varying market penetration rates and traffic demand levels (50% and 100% of peak demand). The results demonstrate that the developed CAV framework significantly improves operational performance in superstreet environments. However, the magnitude of these benefits is highly dependent on traffic volume scales and market penetration rates. The study identifies specific thresholds where CAV adoption begins to yield measurable improvements in efficiency and fuel consumption. The findings indicate that CAVs can effectively leverage SPaT information to optimize speed profiles, thereby reducing stop-and-go behaviors and enhancing throughput. The performance gains vary significantly depending on the mix of CAVs and human-driven vehicles, suggesting that the benefits are not linear but rather contingent on reaching critical penetration levels and specific traffic conditions. This research fills a critical gap in transportation literature by providing empirical evidence on how emerging CAV technologies interact with innovative intersection designs. The findings offer valuable insights for transportation planners and engineers regarding the potential efficiency gains of integrating CAVs into superstreet networks. By establishing the conditions under which CAVs provide operational benefits, the study supports the strategic planning of future infrastructure and traffic control systems. The results also imply that the benefits of CAVs extend beyond conventional intersections, suggesting that innovative geometric designs can further amplify the advantages of connected and autonomous technologies in managing urban traffic congestion and emissions.
Key finding
CAVs significantly improve the operational performance of superstreets, with efficiency gains varying based on traffic volume scales and market penetration rates.
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).
| 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 | — | — | 24 | 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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- Theoretical Contribution: computational model