Can LLMs improve transportation system simulations?
LLM-ABM for Transportation: Assessing the Potential of LLM Agents in System Analysis
April 1, 2025
https://arxiv.org/pdf/2503.22718This paper explores using Large Language Models (LLMs) as agents within a transportation simulation, specifically modeling morning commutes. It investigates whether LLMs can realistically simulate human decision-making in traffic scenarios, focusing on departure time and route choices.
Key LLM-related points:
- LLM Agents as Travelers: LLMs act as individual "traveler" agents, making decisions based on past experiences (memory) and current traffic conditions.
- Cognitive Enhancements: LLMs utilize Chain-of-Thought reasoning, Theory of Mind (awareness of other agents), bounded rationality (not always optimal choices), and self-correction mechanisms.
- System-Level Validation: The simulation's results (traffic flow, arrival times) are compared to established transportation benchmarks to evaluate the LLM agents' effectiveness.
- Imperfect Information & Zero Communication: The multi-agent system simulates a real-world scenario where agents don't have complete information about others and don't communicate directly.
- Potential for Transportation Planning: The research suggests LLM-based agents can be valuable tools for simulating and evaluating transportation systems and policies.