Investigating the Impact of Automated Vehicles on the Safety and Operation of Low‐Speed Urban Networks
DOI: 10.1155/2024/3835831
archive: archived pipeline: cataloged verified
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Summary
This study investigates the simultaneous impact of automated vehicles (AVs) on traffic flow operations and crash risks within low-speed urban networks. While existing literature extensively covers AV impacts at microscopic or mesoscopic levels, this research addresses a gap by analyzing these effects at the network scale. The primary motivation is to understand how AVs influence the trade-off between traffic efficiency and safety, specifically in grid networks with speed limits of 30 km/h and 50 km/h, where intersection safety is critical. The methodology employs simulation experiments using the SUMO simulator on a nine-intersection grid network. The study models mixed traffic flows with AV penetration rates ranging from 0% to 100% in 20% increments. Human-driven vehicles (HDVs) utilize the Wiedemann 99 car-following model, while AVs use the Intelligent Driver Model (IDM) with parameters reflecting Level IV automation, such as reduced time headways and minimum gaps. Traffic performance is assessed using Macroscopic Fundamental Diagrams (MFDs) derived from the Papageorgiou speed-density model. Safety is evaluated using Macroscopic Safety Diagrams (MSDs), which relate traffic density to potential conflicts measured by Time-to-Collision (TTC) surrogate safety measures. To identify optimal operating conditions, the authors apply a multi-objective optimization framework using the NSGA-II genetic algorithm, aiming to maximize traffic flow while minimizing conflict rates. The results demonstrate that the presence of AVs positively impacts system-level capacity, critical density, and average speed, even in low-speed scenarios. Specifically, AVs increase the critical density of the network, allowing the road system to serve more vehicles at capacity while simultaneously decreasing the number of safety conflicts. The multi-objective optimization successfully identified Pareto-optimal solutions that balance high traffic flow with low crash risks. The study also compared the 30 km/h and 50 km/h speed limits, providing insights into how speed regulations interact with AV penetration to affect network performance. The significance of this work lies in its holistic approach to evaluating AV benefits, linking operational efficiency directly with safety outcomes at a macroscopic scale. By establishing the relationship between congestion levels and crash conflicts, the findings offer valuable guidance for traffic planners and operators. The results suggest that AVs can enhance urban network efficiency without compromising safety, supporting policies that encourage AV adoption and informing the design of safe, low-speed urban road networks.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | OpenAlex-citations | — | — | 1 | 2026-06-18 |
| archive | success | core_acuk | — | — | 3 | 2026-06-25 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-18 |
| chunk | success | chunk | — | — | 1 | 2026-06-18 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-18 |
| promote | success | — | — | — | 1 | 2026-06-18 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-18 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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