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arXiv AI

Warned alike, AI agents avoid the less-crowded road while people take it

AI agents built on shared models tend to follow a common forecast, causing them to cluster on one road while avoiding a less crowded alternative. In a two-road congestion game, adding a warning that others might follow a routing tip led 50 GPT agents to crowd one road, raising average travel time from 64 to 95 minutes. Human participants largely maintained balance, but in mixed groups the imbalance grew as more agents were involved, with agents incurring higher travel times than humans.

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Oct 12

Collective Autonomous Vehicle Routing Needs Social Awareness for Coexistence with Human Drivers

The paper proposes adding a social component—marginal cost—to autonomous vehicle (AV) routing rewards to account for the impact of AVs on human-driven vehicles (HDVs). Using multi-agent reinforcement learning on real-world networks, the authors show that this social reward reduces HDV travel time increases and improves overall system performance compared to purely selfish AV routing. A scalable approach limits counterfactual simulations to the most influential AVs, maintaining performance while cutting computational cost.

By Anastasia Psarou, Mikolaj Rams, Blazej Torbus, Rafal Kucharski
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arXiv AI
Oct 5

LLM-Guided Reinforcement Learning with Representative Agents for Traffic Modeling

The paper proposes a scalable traffic modeling approach that uses a single representative large language model (LLM) agent for each homogeneous traveler group, rather than one LLM per traveler. The representative agent maintains a mixed strategy over routes, updates it daily based on positive reinforcement signals, and uses a tunable step size to adjust its strategy. This design improves scalability, stabilizes learning, and produces interpretable dynamics that reproduce realistic behavioral patterns such as the decoy effect and income‑based willingness‑to‑pay differences.

By Hanlin Sun, Jiayang Li
llmsagentsreinforcement-learningmultimodal
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arXiv Machine Learning
3d ago

Learning to Incentivise: Using Reinforcement Learning for Sustainable Urban Mobility

The paper proposes a multi‑agent reinforcement learning framework that dynamically allocates incentives to drivers, encouraging them to take alternative routes. By doing so, it reduces total travel time by up to 16% and CO₂ emissions by 9% under an unlimited budget, while revealing a trade‑off between the two objectives. The study demonstrates that a carefully weighted multi‑objective function can improve both travel time and emissions beyond baseline levels.

By Germ\'an Pardo-Gonz\'alez, Shaghayegh Vosough, Katerina Papadaki, Claudio Roncoli
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Hugging Face Trending Papers
Sep 2

UTP-Bench: Uncertainty-aware Travel Planning Benchmark

UTP‑Bench is a new benchmark that evaluates large language models on uncertainty‑aware travel planning, incorporating real‑world data from 504 Indian cities and empirical delay and crowd patterns. It introduces three metrics—Buffer Adequacy Score, Crowd‑Aware Timing Score, and Transport Delay Absorption Score—to measure how well generated itineraries remain robust under stochastic conditions. Experiments show that current LLMs lag behind human planners in buffering, delay‑aware scheduling, and crowd sensitivity.

llmsmultimodalbenchmarks
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