Spatio-temporal collective data analysis for real-time and proactive navigation

I present a novel approach for real-time and proactive
navigation in crowded environments such as event spaces
and urban areas where many people are moving to their
destinations simultaneously. The challenge is to develop
a real-time navigation system that enables movements of
entire groups to be efficiently guided without causing congestion
by making near-future predictions of people flow.
The proposed approach tries to detect future congestion by using a
spatio-temporal statistical method that predicts people flow.
When future congestion is detected, the approach creates
an optimal navigation plan based on Bayesian optimization
which accounts for the effect of total people flow change
caused by navigation. I also demonstrate the effectiveness of
the navigation approach by computer simulation using artificial
people-flow data.