NaviGNN combines RL and GNNs in multi-agent simulations to achieve 7.8-8.4 minute average commutes, over 89% satisfaction, and above 91% reachability in extreme urban morphologies.
In response to these challenges, governments and private actors are envisioning revolutionary urban models that promise to reshape the way we live and move [2]
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NaviGNN: Multi-Agent Reinforcement Learning and Graph Neural Network for Sustainable Mobility in Futuristic Smart Cities
NaviGNN combines RL and GNNs in multi-agent simulations to achieve 7.8-8.4 minute average commutes, over 89% satisfaction, and above 91% reachability in extreme urban morphologies.