AINeutralarXiv – CS AI · 10h ago6/10
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Dynamic multi-agent deep reinforcement learning-based pricing and incentivization approach in multimodal transportation networks
Researchers propose a multi-agent deep reinforcement learning framework to optimize pricing and incentives across shared mobility services and public transport, balancing competing objectives between authorities, providers, and commuters. Simulations demonstrate the approach reduces congestion by 20%, lowers emissions by 10%, and doubles public transport profit while improving equity.