GTA: Generative Traffic Agents for Simulating Realistic Mobility Behavior
On the map: LLM agents and tool use → Multimodal and embodied agents
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.
Key facts. Cited by 1 paper in the map (data through 7 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.
Builds on
17 of 67 references.
Closest ancestors · 3
Same direction, newest first
- 2025GATSim: Urban Mobility Simulation with Generative Agents29 Jun
- 2024Toward LLM-Agent-Based Modeling of Transportation Systems: A Conceptual Framework9 Dec
- 2024Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation22 Feb
Other references · 14
- 2024The Llama 3 Herd of Modelscited by 14,192
- 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
- 2023A Survey on Large Language Model based Autonomous Agentscited by 2,203
- 2022Social Simulacra: Creating Populated Prototypes for Social Computing Systemscited by 300
- 2023Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectivescited by 253
- 2024Quantifying the Persona Effect in LLM Simulationscited by 164
- 2023The Self-Perception and Political Biases of ChatGPTcited by 66
- 2024ChatSUMO: Large Language Model for Automating Traffic Scenario Generation in Simulation of Urban MObilitycited by 34
- 2024Centaur: a foundation model of human cognitioncited by 34
- 2025Large Language Models Do Not Simulate Human Psychologycited by 17
- 2024AI-Driven Day-to-Day Route Choicecited by 13
- 2021An Overview of Agent-based Traffic Simulatorscited by 11
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Citing papers per year: 2026: 1.
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- 2025Graph RAG as Human Choice Model: Building a Data-Driven Mobility Agent with Preference Chain
- 2026From Survey Personas to LLM Agents: A Generative Agent-based Simulation of Mobility Policy Preference Dynamics
- 2025Deep Generative Model for Human Mobility Behavior
- 2024TravelAgent: Generative Agents in the Built Environment
- 2026CityReal: Human-Aligned Urban Behavior and City Dynamics Simulation with Large-Scale LLM Agents
- 2026When Plausible Is Not Realistic: Evaluating Human Mobility in LLM-Based Urban Simulation