Agent-Based Simulation for Investigating the Safety Concerns of Electric Vehicles in the US
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Summary
This study investigates the pedestrian safety implications of electric vehicle (EV) adoption in the United States, specifically addressing concerns regarding the low auditory detectability of EVs at low speeds. While EVs are quieter than internal combustion engine vehicles (ICEVs), potentially increasing collision risks, EV drivers may exhibit safer behaviors due to higher socioeconomic status and education levels. The research aims to quantify the net effect of these competing factors on pedestrian safety using an agent-based modeling (ABM) approach. The researchers developed a 3D traffic micro-simulation of a real-world signalized intersection in Orlando, Florida, using AnyLogic software. The model simulated interactions between pedestrians, EVs, and ICEVs over a virtual one-year period. Key dynamic parameters included ambient sound levels, ambient illumination, and vehicle flow rates. The simulation defined "near-crashes" as events requiring evasive maneuvers, serving as surrogates for actual crashes. Pedestrian detection of vehicles was modeled using a simplified regression equation based on auditory detectability, which accounted for ambient noise, vehicle speed, and vehicle sound levels. Vehicle stopping sight distance (SSD) was calculated dynamically, incorporating perception-reaction times that were shorter for EV drivers (mean 1.9 seconds) than for ICEV drivers (mean 2.5 seconds) to reflect their presumed higher alertness. A sensitivity analysis was conducted using 9,000 simulations on a supercomputer to evaluate variations in dynamic parameters. The results indicate that EVs pose a 25% higher overall risk to pedestrian traffic safety compared to ICEVs. Although the advantage of shorter stopping sight distances for EV drivers was significant, it was insufficient to compensate for the reduced auditory detectability of silent engines. The study found a statistically significant difference in near-crash risks between the two vehicle types. Additionally, low ambient illumination levels increased the number of near-crashes for both EVs and ICEVs, as pedestrians were more likely to fail to visually detect approaching vehicles. The findings suggest that the inherent silence of EVs remains a critical safety factor that outweighs behavioral advantages in driver caution. The significance of this research lies in its validation of the need for synthetic vehicle sound systems or other auditory warning technologies for EVs. The study demonstrates that while driver behavior differences provide some safety margin, they do not eliminate the elevated risk associated with low-noise vehicles. These findings support regulatory efforts and technological developments aimed at enhancing the auditory detectability of EVs to protect pedestrians, particularly in urban environments with low-light conditions.
Key finding
Electric vehicles pose a 25% higher overall risk to pedestrian traffic safety than internal combustion engine vehicles due to lower auditory detectability.
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 | — | — | 19 | 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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Information type
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- Empirical Findings: crash risk outcomes
- Theoretical Contribution: computational model