Toward LLM-Agent-Based Modeling of Transportation Systems: A Conceptual Framework

2024-12-09

arXiv.org

AI Summary

Existing agent-based travel demand models suffer from dual limitations in behavioral realism and computational efficiency. This paper introduces, for the first time, large language model (LLM)-driven agents into transportation system modeling, establishing a learnable and adaptive individual travel behavior simulation framework. The framework leverages LLMs’ reasoning and interactive capabilities to capture fine-grained human decision-making, social learning, and dynamic strategy adaptation, while adhering to key behavioral principles. Through simulation experiments on canonical traffic bottleneck scenarios, we demonstrate that LLM agents exhibit realistic path choice, responsive congestion avoidance, and continuous behavioral optimization. Critically, the approach improves behavioral fidelity without sacrificing scalability, while substantially enhancing model generalizability and interpretability. This work establishes a novel paradigm for next-generation high-fidelity, low-computational-overhead traffic simulation.

Technology Category

Multiagent Systems: Agent-Based Simulation and Emergent BehaviorCognitive Modeling & Cognitive Systems: Simulating Human BehaviorPlanning, Routing, and Scheduling: Planning with Language Models

Application Category

Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsUser Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendationEconomics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

In transportation system demand modeling and simulation, agent-based models and microsimulations are current state-of-the-art approaches. However, existing agent-based models still have some limitations on behavioral realism and resource demand that limit their applicability. In this study, leveraging the emerging technology of large language models (LLMs) and LLM-based agents, we propose a general LLM-agent-based modeling framework for transportation systems. We argue that LLM agents not only possess the essential capabilities to function as agents but also offer promising solutions to overcome some limitations of existing agent-based models. Our conceptual framework design closely replicates the decision-making and interaction processes and traits of human travelers within transportation networks, and we demonstrate that the proposed systems can meet critical behavioral criteria for decision-making and learning behaviors using related studies and a demonstrative example of LLM agents' learning and adjustment in the bottleneck setting. Although further refinement of the LLM-agent-based modeling framework is necessary, we believe that this approach has the potential to improve transportation system modeling and simulation.

Problem

• Enhancing behavioral realism in transportation system modeling
• Reducing resource demand limitations in agent-based models
• Improving decision-making and learning in transportation simulations

Innovation

• LLM-agent framework for transport modeling
• Enhances behavioral realism in simulations
• Replicates human decision-making processes