Optimization of real-time collective navigation

We are developing a technology for finding efficient navigation of moving crowds of people or vehicles. This technology predicts upcoming risks of congestion caused by the crowds and searches for the collectively optimal navigation to avoid the congestion. It is difficult for humans to figure out when, where, and how they should navigate the moving crowds to ease congestion. We present an algorithm for deriving a collectively optimal navigation using Bayesian optimization that evaluates which navigation contributes to solving congestion by various simulations. We further envision an advanced and adaptive navigation by incorporating real-time sensing data of people and vehicles. Our technology can navigate people on the fly and establish secure and comfortable event operations as well as stabilized infrastructures.

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