  Understanding human mobility is important for the development of intelligent
mobile service robots as it can provide prior knowledge and predictions of
human distribution for robot-assisted activities. In this paper, we propose a
probabilistic method to model human motion behaviors which is determined by
both internal and external factors in an indoor environment. While the internal
factors are represented by the individual preferences, aims and interests, the
external factors are indicated by the stimulation of the environment. We model
the randomness of human macro-level movement, e.g., the probability of visiting
a specific place and staying time, under the Bayesian framework, considering
the influence of both internal and external variables. We use two case studies
in a shopping mall and in a college student dorm building to show the
effectiveness of our proposed probabilistic human mobility model. Real
surveillance camera data are used to validate the proposed model together with
survey data in the case study of student dorm.
