An Information Theory Based Method for Quantifying the Predictability of Human Mobility

Research on human mobility drives the development of economy and society. How to predict when and where one will go accurately is one of the core research questions. Existing work is mainly concerned with performance of mobility prediction models. Since accuracy of predict models does not indicate whether or not one's mobility is inherently easy to predict, there has not been a definite conclusion about that to what extent can our predictions of human mobility be accurate. To help solve this problem, we describe the formalized definition of predictability of human mobility, propose a model based on additive Markov chain to measure the probability of exploration, and further develop an information theory based method for quantifying the predictability considering exploration of human mobility. Then, we extend our method by using mutual information in order to measure the predictability considering external influencing factors, which has not been studied before. Experiments on simulation data and three real-world datasets show that our method yields a tighter upper bound on predictability of human mobility than previous work, and that predictability increased slightly when considering external factors such as weather and temperature.