Evaluating Large Language Models for Decision-Making in Agent-Based Urban Mobility Simulations

2026-07-02

AI Summary

This study addresses the limited adaptability and contextual responsiveness of traditional rule-based multi-agent traffic simulations in dynamic environments. To overcome this, the authors propose a novel hybrid architecture that integrates a large language model (LLM) as a complementary cognitive layer within the GAMA simulation platform. The LLM is invoked via API to dynamically assess the need for route replanning and is augmented with a persistent memory mechanism to enhance behavioral consistency. While preserving conventional path-planning algorithms, this approach significantly improves agents’ situational awareness and decision-making flexibility. Experimental results demonstrate that, in highly dynamic scenarios, LLM-augmented agents exhibit superior adaptability and behavioral coherence, thereby validating the potential of LLMs for modeling spatially explicit multi-agent systems in urban traffic simulation.

Abstract

Urban mobility modeling faces challenges in representing decision-making in dynamic environments. Although Multi-Agent Systems are widely used, rule-based approaches rely on fixed heuristics that limit adaptive behavior. This work investigates the integration of Large Language Models (LLMs) as decision-making components in multi-agent simulations. We propose a hybrid architecture that connects the GAMA platform to an external LLM-based module through an API, enabling agents to determine whether route replanning is necessary. Rather than replacing routing algorithms, the LLM serves as a decision layer that guides replanning behavior. The approach incorporates persistent memory, allowing past interactions to influence future decisions and promote behavioral consistency. We compare rule-based and LLM-assisted approaches across multiple road-blockage scenarios and population scales. Results indicate that LLM-enabled agents exhibit greater adaptability and contextual awareness, particularly in scenarios with higher route flexibility. Memory influences performance and behavioral consistency, with effects varying across configurations. Overall, LLMs serve as complementary cognitive layers that enrich behavioral representations in urban mobility simulations and hold potential for modeling complex decision-making in spatial multi-agent systems.

Problem

• Urban mobility
• Decision-making
• Multi-Agent Systems
• Adaptive behavior
• Large Language Models

Innovation

• Large Language Models
• Multi-Agent Simulation
• Urban Mobility
• Decision-Making Architecture
• Persistent Memory