Real-time and proactive navigation via spatio-temporal prediction

We 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. Our 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. Our approach tries to detect future congestion by using a spatio-temporal statistical method that predicts people flow. When future congestion is detected, our approach creates an optimal navigation plan based on "what-if" simulations, which accounts for the effect of total people flow change caused by navigation. We experimentally compare the spatio-temporal statistical method with the conventional matrix factorization based approach using a real data set. We also demonstrate the effectiveness of our navigation approach by computer simulation using artificial people-flow data.