# GTA: Generative Traffic Agents for Simulating Realistic Mobility Behavior

arXiv: https://arxiv.org/abs/2601.16778 · HTML: https://mlsanity.com/paper/2601.16778/

## Key facts

- Cited by 1 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 17 of its 67 references that are in the map.
- First posted on arXiv 23 Jan 2026.

## Placed in

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

## Builds on (most cited first, 17 of 17)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [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)
- [Social Simulacra: Creating Populated Prototypes for Social Computing Systems](https://mlsanity.com/paper/2208.04024/) (2022)
- [Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives](https://mlsanity.com/paper/2312.11970/) (2023)
- [Quantifying the Persona Effect in LLM Simulations](https://mlsanity.com/paper/2402.10811/) (2024)
- [Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation](https://mlsanity.com/paper/2402.14744/) (2024)
- [The Self-Perception and Political Biases of ChatGPT](https://mlsanity.com/paper/2304.07333/) (2023)
- [Centaur: a foundation model of human cognition](https://mlsanity.com/paper/2410.20268/) (2024)
- [ChatSUMO: Large Language Model for Automating Traffic Scenario Generation in Simulation of Urban MObility](https://mlsanity.com/paper/2409.09040/) (2024)
- [Toward LLM-Agent-Based Modeling of Transportation Systems: A Conceptual Framework](https://mlsanity.com/paper/2412.06681/) (2024)
- [Large Language Models Do Not Simulate Human Psychology](https://mlsanity.com/paper/2508.06950/) (2025)
- [AI-Driven Day-to-Day Route Choice](https://mlsanity.com/paper/2412.03338/) (2024)
- [An Overview of Agent-based Traffic Simulators](https://mlsanity.com/paper/2102.07505/) (2021)
- [GATSim: Urban Mobility Simulation with Generative Agents](https://mlsanity.com/paper/2506.23306/) (2025)
- [`Truncate, replicate, sample': a method for creating integer weights for spatial microsimulation](https://mlsanity.com/paper/1303.5228/) (2013)
- [Patterns of Urban Foot Traffic Dynamics](https://mlsanity.com/paper/1910.02380/) (2019)

## Built on by (most cited first, 1 of 1)

- [AGORA: Can Deliberation and Governance Gates Absorb Participation Bias in Transit Planning?](https://mlsanity.com/paper/2606.13696/) (2026)

## Abstract (arXiv)

People's transportation choices reflect complex trade-offs shaped by personal preferences, social norms, and technology acceptance. Predicting such behavior at scale is a critical challenge with major implications for urban planning and sustainable transport. Traditional methods use handcrafted assumptions and costly data collection, making them impractical for early-stage evaluations of new technologies or policies. We introduce Generative Traffic Agents (GTA) for simulating large-scale, context-sensitive transportation choices using LLM-powered, persona-based agents. GTA generates artificial populations from census-based sociodemographic data. It simulates activity schedules and mode choices, enabling scalable, human-like simulations without handcrafted rules. We evaluate GTA in Berlin-scale experiments, comparing simulation results against empirical data. While agents replicate patterns, such as modal split by socioeconomic status, they show systematic biases in trip length and mode preference. GTA offers new opportunities for modeling how future innovations, from bike lanes to transit apps, shape mobility decisions.