Research map / Multi-agent systems and games / Human-AI and goal-conditioned
Traffic Optimization for a Mixture of Self-Interested and Compliant Agents
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the UE linear program to determine the minimum compliant share and assign compliant flow to achieve system optimum | Sioux Falls traffic network · Sioux Falls | total travel time: 7,480,225 -> 7,194,256; 13.04% compliant; 3.82% improve | Helped |
| Use the UE linear program to determine the minimum compliant share and assign compliant flow to achieve system optimum | Eastern MA traffic network · Eastern MA | total travel time: 28,181 -> 27,323; 19.73% compliant; 3.04% improve | Helped |
| Use the UE linear program to determine the minimum compliant share and assign compliant flow to achieve system optimum | Anaheim traffic network · Anaheim | total travel time: 1,419,913 -> 1,395,015; 19.76% compliant; 1.75% improve | Helped |
| Use the UE linear program to determine the minimum compliant share and assign compliant flow to achieve system optimum | Chicago S traffic network · Chicago S | total travel time: 18,377,329 -> 17,953,267; 27.29% compliant; 2.31% improve | Helped |
| Use the UE linear program to determine the minimum compliant share and assign compliant flow to achieve system optimum | Philadelphia traffic network · Philadelphia | total travel time: 335,647,106 -> 324,268,465; 49.59% compliant; 3.39% improve | Helped |
| Use the UE linear program to determine the minimum compliant share and assign compliant flow to achieve system optimum | Chicago R traffic network · Chicago R | total travel time: 33,656,964 -> 31,942,956; 53.34% compliant; 5.09% improve | Helped |
| Require self-interested flow to use paths that minimize both latency and marginal cost (ablation) | UE linear program on the illustrated network · Illustrated network instance | maximum UE flow: 2 \cdot \sqrt{2/3} -> 1 + 2 \cdot \sqrt{2/3} | Helped |
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