Modeling High Predictability and Scaling Laws of Human Mobility

Previous studies suggest that human mobility is highly regular in two respects. Firstly, individual's travels are governed by occasional exploration of new locations and preferential return to most frequently visited locations. Secondly, human mobility sequences exhibit high predictability. The existing model [1] is able to mimic exploration and preferential return, and fit actual mobility data. However, the high predictability issue is not addressed in this model. In this paper, we derive an upper bound of the predictability manifested by this model. Motivated by the incorrigible gap between the bound and the empirical results shown in [2], we further propose a new Markovian model by modifying the rule of preferential return to be conditional on the individuals' current location. We show both theoretically and empirically that the new Markovian model presents high predictability while preserving the desirable scaling properties of the original model in [1], making it the most complete model to date in capturing the essence of human mobility.