Mathematical analysis based on the Macroscopic Fundamental Diagram proves road transportation networks are fragile, with a skewness indicator for cross-network comparison and simulations showing stochastic reinforcement.
Transportation Research Part B: Methodological 42, 771–781
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Multi-agent DRL framework shows dynamic incentives and pricing can cut commuter costs ~20%, emissions ~10%, and double public transport profit in simulated morning peak scenarios.
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The fragile nature of road transportation networks
Mathematical analysis based on the Macroscopic Fundamental Diagram proves road transportation networks are fragile, with a skewness indicator for cross-network comparison and simulations showing stochastic reinforcement.
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Dynamic multi-agent deep reinforcement learning-based pricing and incentivization approach in multimodal transportation networks
Multi-agent DRL framework shows dynamic incentives and pricing can cut commuter costs ~20%, emissions ~10%, and double public transport profit in simulated morning peak scenarios.