Characterization of human mobility based on Information Theory quantifiers

Abstract

Location-aware services provide valuable information for capturing human mobility patterns. In this context, analyzing the mobility dynamics, such as the means of transportation and their speeds, leads to better solutions by understanding the underlying data generating process and identifying different patterns. Strategies based on extracting Information Theory measures associated with ordinal patterns methods, for example, Complex-Entropy Causality Plane and Fisher–Shannon Causality Plane, have reached relevant advancements in distinguishing different time series dynamics. Thus, they are promising tools to explain those complex behaviors to improve human mobility-based services. In this work, we aim to characterize the users’ means of transportation based on their speed time series derived from the Geolife dataset. Therefore, for each type of transportation, we observe the speed dynamics over time and correlate their associated Information Theory quantifiers with colored noises mapped onto the causal planes. Evaluation results show the potential of our study, allowing us to distinguish motorized and non-motorized means of transportation. Also, based on that mapping, we can estimate the transportation switching.