Research map / Multi-agent systems and games / Human-AI and goal-conditioned
Multiagent-Based Route Guidance for Increasing the Chance of Arrival on Time
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| reformulate route assignment from MIQP to MILP with linearization variables (ablation) | multiagent route guidance · Singapore, 1,600 vehicles | avg computation time (s): 11.61 -> 1.19 | Helped |
| reformulate MIQP as MILP (ablation) | multiagent route guidance · New York, 1,600 vehicles | avg computation time (s): 6.98 -> 0.72 | Helped |
| add real-time information from 1-2 hops of neighboring infrastructure agents (E=1,2) (ablation) | multiagent route guidance · Singapore, 1,200 vehicles | average probability of arriving on time | Helped |
| increase communication hops to E=3,4 (ablation) | multiagent route guidance · Singapore, 1,200 vehicles | average probability of arriving on time | No clear effect |
| add real-time information with E=1,2,3 hops (ablation) | multiagent route guidance · New York, 1,200 vehicles | average probability of arriving on time | Helped |
| deadline-aware route assignment versus LET-based adaptive routing (RIS, tau-rerouting) | adaptive route guidance · Singapore and New York, deadline levels alpha 0.4-1.4 | average probability of arriving on time | Helped |
| lower intention-sharing penetration rate (0.4 vs 0.8) (ablation) | adaptive methods · Singapore and New York, 1,200 vehicles | average probability of arriving on time | Hurt |
| adaptive vs pre-computed routing in sparse or oversaturated traffic | route guidance · Singapore 400/1,600, New York 400 | average probability of arriving on time | No clear effect |
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