Abstract
Knowledge about space usage from variables such as density and walking speed could support a variety of service applications. However, there is not much knowledge on how the usage of space changes during extended periods of time and what affects the changes. We have installed a person tracking system in a large area of a shopping center and collected pedestrian data over a year. In this paper, we analyze the collected data to find the changes in pedestrian density and speed, percentage of children, and pedestrian trajectories. The changes from day to day, as well as during the day are examined, and a number of factors that affect them are identified. This is in turn used in the prediction of the state of the space using a Gaussian process model.
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