mlsanityData to 14 Oct 2026

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

arXiv 2412.06681 ↗First posted 9 Dec 2024cs.AI cs.MAcited by 19

On the map: LLM agents and tool use → Multimodal and embodied agents

Abstract (arXiv)

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.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. Builds on 65 of its 120 references that are in the map. First posted on arXiv 9 Dec 2024.

Builds on

65 of 120 references.

Closest ancestors · 3

Same direction, newest first

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  2. 2024Be More Real: Travel Diary Generation Using LLM Agents and Individual Profiles10 Jul
  3. 2024Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation22 Feb

Other references · 62

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  16. 2023EconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic Activitiescited by 154
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  19. 2024OASIS: Open Agent Social Interaction Simulations with One Million Agentscited by 132
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 12; 2026: 7.

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  2. 2025A Large Language Model for Feasible and Diverse Population Synthesiscited by 9
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  4. 2025LLM-Agent-Controller: A Universal Multi-Agent Large Language Model System as a Control Engineercited by 5
  5. 2025Aligning LLM agents with human learning and adjustment behavior: a dual agent approachcited by 3
  6. 2025LLM-Guided Reinforcement Learning with Representative Agents for Traffic Modelingcited by 2
  7. 2025Benchmarking Retrieval-Augmented Generation Strategies for Large Language Model-Based Travel Mode Choice Predictioncited by 1
  8. 2025EvolveSignal: A Large Language Model Powered Coding Agent for Discovering Traffic Signal Control Strategiescited by 1
  9. 2025Modeling realistic human behavior using generative agents in a multimodal transport system: Software architecture and Application to Toulousecited by 1
  10. 2026A Vision-and-Knowledge Enhanced Large Language Model for Generalizable Pedestrian Crossing Behavior Inferencecited by 1
  11. 2026Can large language models interpret unstructured chat data on dynamic group decision-making processes? Evidence on joint destination choicecited by 1
  12. 2026GTA: Generative Traffic Agents for Simulating Realistic Mobility Behaviorcited by 1
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  1. 2025IMAIA: Interactive Maps AI Assistant for Travel Planning and Geo-Spatial Intelligencecited by 0
  2. 2025Validating Generative Agent-Based Models for Logistics and Supply Chain Management Researchcited by 0
  3. 2025Generating Individual Travel Diaries Using Large Language Models Informed by Census and Land-Use Datacited by 0
  4. 2026AGORA: Can Deliberation and Governance Gates Absorb Participation Bias in Transit Planning?cited by 0
  5. 2026GenWorld: Empirically Grounded Urban Simulation Infrastructure for Scalable LLM-Agent Studiescited by 0
  6. 2026An Agentic Approach for Active Data Collection, Travel Behavior Modeling, and Weather-Sensitive Demand Predictioncited by 0
  7. 2026CALM: A Calibrated LLM Choice Network Framework for Activity-Based Traveler Simulationcited by 0

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