GTA: Generative Traffic Agents for Simulating Realistic Mobility Behavior
This addresses the problem of scalable mobility simulation for urban planners, but it is incremental as it builds on existing agent-based methods with LLM enhancements.
The paper tackled the challenge of predicting large-scale transportation choices for urban planning by introducing Generative Traffic Agents (GTA), which simulate mobility behavior using LLM-powered agents, but the agents showed systematic biases in trip length and mode preference in Berlin-scale experiments.
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.