# LLM-ABM for Transportation: Assessing the Potential of LLM Agents in System Analysis

arXiv: https://arxiv.org/abs/2503.22718 · HTML: https://mlsanity.com/paper/2503.22718/

## Key facts

- Cited by 6 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 14 of its 49 references that are in the map.
- First posted on arXiv 25 Mar 2025.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 14 of 14)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [Exploring Large Language Models for Communication Games: An Empirical Study on Werewolf](https://mlsanity.com/paper/2309.04658/) (2023)
- [When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs](https://mlsanity.com/paper/2406.01297/) (2024)
- [S3: Social-network Simulation System with Large Language Model-Empowered Agents](https://mlsanity.com/paper/2307.14984/) (2023)
- [Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View](https://mlsanity.com/paper/2310.02124/) (2023)
- [Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation](https://mlsanity.com/paper/2402.14744/) (2024)
- [Humanoid Agents: Platform for Simulating Human-like Generative Agents](https://mlsanity.com/paper/2310.05418/) (2023)
- [Epidemic Modeling with Generative Agents](https://mlsanity.com/paper/2307.04986/) (2023)
- [Network Formation and Dynamics Among Multi-LLMs](https://mlsanity.com/paper/2402.10659/) (2024)
- ["Guinea Pig Trials" Utilizing GPT: A Novel Smart Agent-Based Modeling Approach for Studying Firm Competition and Collusion](https://mlsanity.com/paper/2308.10974/) (2023)
- [Day-to-Day Dynamic Traffic Assignment with Imperfect Information, Bounded Rationality and Information Sharing](https://mlsanity.com/paper/1811.10729/) (2018)

## Built on by (most cited first, 6 of 6)

- [Aligning LLM agents with human learning and adjustment behavior: a dual agent approach](https://mlsanity.com/paper/2511.00993/) (2025)
- [LLM-Guided Reinforcement Learning with Representative Agents for Traffic Modeling](https://mlsanity.com/paper/2511.06260/) (2025)
- [LLM-based Agents for Automated Confounder Discovery and Subgroup Analysis in Causal Inference](https://mlsanity.com/paper/2508.07221/) (2025)
- [AI Playing Business Games: Benchmarking Large Language Models on Managerial Decision-Making in Dynamic Simulations](https://mlsanity.com/paper/2509.26331/) (2025)
- [CALM: A Calibrated LLM Choice Network Framework for Activity-Based Traveler Simulation](https://mlsanity.com/paper/2609.22252/) (2026)
- [Warned alike, AI agents avoid the less-crowded road while people take it](https://mlsanity.com/paper/2609.30883/) (2026)

## Abstract (arXiv)

Agent-based modeling approaches represent the state-of-art in modeling travel demand and transportation system dynamics and are valuable tools for transportation planning. However, established agent-based approaches in transportation rely on multi-hierarchical mathematical models to simulate travel behavior, which faces theoretical and practical limitations. The advent of large language models (LLM) provides a new opportunity to refine agent-based modeling in transportation. LLM agents, which have impressive reasoning and planning abilities, can serve as a proxy of human travelers and be integrated into the modeling framework. However, despite evidence of their behavioral soundness, no existing studies have assessed the impact and validity of LLM-agent-based simulations from a system perspective in transportation. This paper aims to address this issue by designing and integrating LLM agents with human-traveler-like characteristics into a simulation of a transportation system and assessing its performance based on existing benchmarks. Using the classical transportation setting of the morning commute, we find that not only do the agents exhibit fine behavioral soundness, but also produce system dynamics that align well with standard benchmarks. Our analysis first verifies the effectiveness and potential of LLM-agent-based modeling for transportation planning on the system level.