GTA: Generative Traffic Agents for Simulating Realistic Mobility Behavior

TL;DR

GTA introduces a scalable, AI-driven simulation framework for realistic transportation behavior modeling, enabling urban planning insights without extensive data collection.

Contribution

The paper presents Generative Traffic Agents, a novel LLM-based approach for simulating large-scale, context-aware mobility choices using sociodemographic data.

Findings

Agents replicate modal split patterns by socioeconomic status

Simulation captures realistic activity schedules and mode choices

Systematic biases observed in trip length and mode preference

Abstract

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.