How can I model decreasing road costs in multi-agent traffic assignment?
Synergistic Traffic Assignment
February 10, 2025
https://arxiv.org/pdf/2502.04343This paper introduces Synergistic Traffic Assignment (STA), where road usage cost decreases with more users, modeling shared transportation benefits. It contrasts with Avoidant Traffic Assignment (ATA), where costs increase with congestion.
Key points for LLM-based multi-agent systems:
- Simultaneous, impact-blind best response converges: This simplifies agent decision-making and enables efficient equilibrium computation using optimized pathfinding algorithms (customizable contraction hierarchies). Agents choose optimal paths based on current road costs without considering their impact or others' simultaneous changes. This could be a valuable simplification for LLM agents in shared environments.
- Fast equilibrium calculation: STA equilibria are reached quickly (under 20 iterations in experiments), unlike ATA. This could enable near real-time coordination of LLM agents in collaborative tasks.
- Sharing potential identification: STA highlights areas with high sharing potential, crucial for designing effective shared transportation systems. This could be used by LLM agents to optimize resource allocation and collaboration strategies.
- Application in bus line planning: The paper demonstrates STA's use in optimizing bus routes for minimal travel time, showcasing its potential for optimizing real-world multi-agent systems. LLM agents could use similar principles to coordinate actions and resource usage in shared environments.