Emerging Behavioral Adaptation of Human-Driven Vehicles in Interactions with Automated Vehicles: Insights from a Microsimulation Study
DOI: 10.3390/futuretransp5030124
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Summary
This study investigates how human-driven vehicles (HDVs) adapt their driving behavior when interacting with automated vehicles (AVs) during the transitional phase of mixed traffic. While prior research has largely focused on network-level traffic efficiency, this work addresses a gap in understanding individual-level behavioral changes in human drivers influenced by AV market penetration rates (MPRs) and driving styles. The authors aimed to determine if HDVs adjust their spacing, speed, and maneuvering behaviors in response to aggressive versus cautious AVs at MPRs of 0%, 25%, 50%, and 75%. The methodology combined a driving simulator experiment with microsimulation modeling. First, trajectory data were collected from 160 participants in a dynamic driving simulator to capture realistic HDV car-following behaviors. This empirical data was used to calibrate the Wiedemann 99 car-following model in VISSIM software, ensuring accurate representation of human driver heterogeneity. AVs were modeled using VISSIM’s adaptive cruise control model, configured with a 1-second time headway for aggressive styles and a 3-second headway for cautious styles. The microsimulation analyzed ten runs per scenario on a 12 km highway segment under Level of Service D conditions. Behavioral metrics included average time headway (THW), relative velocity (RelVel), average acceleration (Acc), average deceleration (Dec), and lane change frequency (LnCh). Statistical analysis employed two-way ANOVA with Box–Cox transformations to assess the effects of MPR and AV driving style. Results indicated that higher AV MPRs significantly decreased HDV THW, average acceleration, and average deceleration, suggesting smoother and closer following behavior as automation increased. The influence of AV driving style varied by metric: relative velocity increased when HDVs followed cautious AVs but decreased when following aggressive AVs. Lane change frequency trends mirrored these patterns, with HDVs adapting their maneuvering frequency based on the predictability and spacing of surrounding AVs. These findings confirm that human drivers do not maintain static behaviors but actively adapt to the prevailing traffic composition and the specific longitudinal control strategies of automated vehicles. The significance of this research lies in its demonstration that neglecting HDV behavioral adaptation can lead to inaccurate traffic flow assessments and ineffective policy-making during the transition to full automation. By providing empirically calibrated models that account for human-AV interactions, the study offers a more realistic framework for evaluating mixed traffic scenarios. The findings imply that traffic management strategies and AV design parameters must consider the reciprocal behavioral adjustments of human drivers to ensure safety and efficiency in mixed-traffic environments.
Key finding
Higher automated vehicle market penetration rates caused human-driven vehicles to decrease their time headway, acceleration, and deceleration, with relative velocity changes depending on whether the automated vehicles were driving aggressively or cautiously.
Methodology
simulation_modeling
Sample size: 160
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 author_sweep_intake on 2026-05-28.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 2 | 2026-05-28 |
| archive | success | openalex | — | — | 9 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | — | — | — | 1 | 2026-05-28 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 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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- Empirical Findings: behavioral performance data