Potential impact of autonomous vehicles in mixed traffic from simulation using real traffic flow
DOI: 10.26599/jicv.2023.9210001
archive: archived pipeline: cataloged verified
Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)
Summary
This study investigates the potential impact of autonomous vehicles (AVs) on mixed traffic conditions, specifically addressing the transition period from manually driven to fully autonomous fleets. Motivated by projections that 50% of new vehicles will be autonomous by 2040, the research aims to identify driving characteristics and parameter values that optimize safety and efficiency when AVs interact with manually controlled vehicles (MCs). The authors note that while previous studies have examined AV impacts in isolation or at intersections, there is a lack of broad analysis regarding how different AV driving styles affect mixed traffic flow on urban roads. The methodology employs the Simulation of Urban MObility (SUMO) software to simulate traffic on a 20 km section of high-speed roads in Gothenburg, Sweden. To ensure realism, the simulation uses real traffic flow and speed data collected by the Swedish Transport Administration in 2002 and 2019. The authors calibrated the Krauß car-following model, selected for its superior alignment with real-world measurements compared to the Intelligent Driver Model. Simulations varied AV penetration rates and adjusted specific AV parameters, including `speedFactor`, `apparentDecel`, `lcStrategic`, and `decel`. Performance was evaluated using fundamental diagrams, the number of lane changes, and the number of conflicts, which serve as proxies for traffic efficiency and safety. The results indicate that traffic flow in Gothenburg increased significantly between 2002 and 2019, correlating with population growth, with the most substantial increases occurring during morning commute hours. Crucially, the study finds that the AV parameters that enhance safety and efficiency in a 100% autonomous environment differ from those optimal for mixed traffic. For instance, specific adjustments to deceleration and lane-changing strategies are required to mitigate conflicts in mixed scenarios. The analysis demonstrates that static AV behaviors are insufficient; rather, AVs must adapt their driving styles based on the surrounding traffic composition. The significance of this work lies in its recommendation that AVs should possess the ability to switch between mixed and autonomous driving styles depending on the scenario. This adaptability is necessary to maximize road safety and efficiency during the transitional phase of AV adoption. Furthermore, the findings suggest that infrastructure rules, such as lane usage regulations, may need adaptation to accommodate mixed traffic effectively. The study provides specific parameter recommendations for intelligent vehicle design, emphasizing that optimizing AV behavior for mixed traffic requires distinct strategies compared to fully autonomous environments.
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.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-20 |
| archive | success | openalex | — | — | 5 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-20 |
| chunk | success | chunk | — | — | 1 | 2026-06-20 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-20 |
| enrich | success | openalex | — | — | 1 | 2026-06-20 |
| promote | success | — | — | — | 1 | 2026-06-20 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-20 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.