PeRP: Personalized Residual Policies For Congestion Mitigation Through Co-operative Advisory Systems
DOI: 10.1109/itsc57777.2023.10422444
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Summary
This paper addresses the challenge of mitigating traffic congestion in mixed-autonomy environments where human drivers do not perfectly follow advisory instructions. While existing Piecewise Constant (PC) policies effectively stabilize traffic flow by providing human-compatible speed advice, they assume a "one-size-fits-all" approach that ignores individual variations in driver behavior. To overcome this limitation, the authors propose Personalized Residual Policies (PeRP), a cooperative advisory system that tailors congestion mitigation advice to specific driver traits, such as aggressive or conservative driving styles. The methodology combines unsupervised driver trait inference with residual policy learning. First, a Variational Autoencoder (VAE) infers a driver’s latent trait from their driving trajectory, capturing how they deviate from advised speeds (e.g., driving faster or slower than recommended). This inferred trait is then conditioned into a residual policy that adjusts the base PC policy’s output. The system was evaluated in simulation using the Flow framework and SUMO traffic simulator on a single-lane circular track with 40 vehicles. The ego vehicle followed the PeRP advice, while other vehicles followed an Intelligent Driver Model. The driver model was designed to exhibit imperfect instruction adherence, with traits defined as Gaussian offsets from the advised speed. Experimental results demonstrate that PeRP successfully mitigates congestion while adapting to diverse driver behaviors. The VAE effectively clustered trajectories according to driving styles in the latent space. Compared to baseline methods, including optimal speed limits and standard PC policies, PeRP achieved a 4% to 22% improvement in average vehicle speed, depending on the specific driver trait and policy hold-length. The system also reduced the standard deviation of speeds, indicating smoother traffic flow. Crucially, PeRP achieved these gains with significantly less training overhead than training personalized policies from scratch, as it leverages pre-trained base policies. The significance of this work lies in its ability to bridge the gap between theoretical optimal control and practical human-in-the-loop systems. By accounting for the uncertainty and variability of human behavior, PeRP offers a more robust solution for cooperative advisory systems. This approach allows for effective congestion mitigation without requiring precise control over autonomous fleets, making it applicable to real-world scenarios where human drivers retain control. The findings suggest that personalizing advisory inputs based on inferred driver traits can substantially enhance the performance of traffic management systems.
Key finding
The proposed Personalized Residual Policies improved average traffic speed by 4% to 22% over baseline methods by adapting congestion mitigation advice to inferred individual driver traits.
Methodology
simulation_modeling
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 | canonical_url | — | — | 7 | 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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- Theoretical Contribution: computational model