Can LLMs improve transport system modeling?
TOWARD LLM-AGENT-BASED MODELING OF TRANSPORTATION SYSTEMS: A CONCEPTUAL FRAMEWORK
December 10, 2024
https://arxiv.org/pdf/2412.06681This paper proposes a new framework for modeling transportation systems using LLM-powered agents. Instead of traditional equation-based models, it uses LLMs to simulate individual traveler behavior within a dynamic traffic network. Key points relevant to LLM-based multi-agent systems include:
- Human-aligned Agent Design: Agents have memory (short and long-term), identity, and an LLM core for decision-making, mirroring human cognitive processes.
- Behavioral Tuning: Demonstrates the feasibility of aligning LLM agent behavior with human travel choices (activity scheduling, destination, mode) using techniques like few-shot learning and persona discovery.
- Learning and Adjustment: Agents adapt their decisions based on feedback from the simulated environment (e.g., adjusting departure times in a bottleneck scenario), mimicking human learning through mechanisms like chain-of-thought prompting, bounded rationality, and theory of mind.
- Reduced Data Requirements: Leveraging pre-trained LLMs reduces the need for extensive data collection and calibration.
- Simplified Scenario Evaluation: Changing agent behavior through prompts simplifies testing different scenarios (new roads, policies, technologies) compared to modifying traditional models.
- Challenges: Addresses scalability limitations of simulating large numbers of LLM agents and the need for robust verification methods.