AI & Generative Models
GTA: Generative Traffic Agents for Simulating Realistic Mobility Behavior
This paper introduces Generative Traffic Agents (GTA), a novel framework for simulating realistic, context-sensitive mobility behavior. Leveraging LLM-powered persona-based agents grounded in population statistics, GTA enables large-scale, human-like traffic simulations without manual rule crafting. The system generates artificial populations from census data, simulates activity schedules and mode choices, and integrates with traffic simulators like SUMO. Evaluated in Berlin, GTA replicates modal split patterns by socioeconomic status but shows biases in trip length and mode preference. This approach offers a powerful, rapid prototyping tool for urban planning and mobility innovation.
Key Metrics & Immediate Impact
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Fidelity to Real-World Patterns
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Reduced Manual Rule Crafting
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Faster Innovation Cycles
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Actionable Insights from LLMs
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
LLM-Powered Personalization
Generates diverse, realistic agent personas from census data, enabling context-sensitive decision-making without handcrafted rules.
GTA Simulation Workflow
Population Sampling
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Persona Description Generation
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Activity Scheduling
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Trip Planning & Route Generation
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Dynamic User Equilibrium
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Traffic Simulation Execution
GTA in Action: Berlin Mobility Simulation
GTA successfully simulated 10% of Berlin's population (35,769 agents), replicating key modal split patterns by socioeconomic status against empirical data. While showing systematic biases in trip length and mode preference (e.g., underrepresentation of very short trips, overestimation of cycling), the system captures realistic temporal trends in traffic flow and offers interpretable insights into mobility choices. This demonstrates its potential for early-stage evaluation of urban mobility innovations.
Policy Evaluation Catalyst
Enables rapid, low-cost evaluation of new mobility policies (e.g., fare-free transport, bike lane investments) before real-world deployment.
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Simulating Disruptions & Innovations
GTA's integration with SUMO allows detailed vehicle-level simulations, capturing dynamic effects like speed adjustments, rerouting, and congestion. This makes it particularly valuable for analyzing the impact of disruptions or new mobility innovations (e.g., automated vehicles, e-scooters) by providing high-resolution insights into resulting traffic flows and user behavior changes.
Situated AI for HCI
Grounds generative agents in real-world urban environments and population data, making them reliable for HCI simulations beyond narrative plausibility.
GTA's Modular Architecture for HCI Research
Profile Module (Population & Persona)
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Planning Module (Activity & Trip Scheduling)
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Action Module (Traffic Simulation & Routing)
| Aspect | Prior LLM-based HCI Agents | GTA (Generative Traffic Agents) |
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Calculate Your Potential ROI
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Estimated Annual Savings
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Your AI Implementation Roadmap
A phased approach to integrate generative AI, ensuring maximum impact with minimal disruption.
Phase 1: Discovery & Strategy
Comprehensive analysis of your existing workflows, identification of high-impact AI opportunities, and development of a tailored implementation strategy.
Phase 2: Pilot & Proof-of-Concept
Rapid prototyping and deployment of AI solutions in a controlled environment to validate effectiveness and refine models based on real-world feedback.
Phase 3: Scaled Integration
Full-scale deployment across relevant departments, seamless integration with your existing systems, and comprehensive training for your teams.
Phase 4: Optimization & Future-Proofing
Continuous monitoring, performance optimization, and strategic planning for future AI advancements and evolving business needs.
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