Daily Human Mobility: A Reproduction Model and Insights from the Energy Concept

Abstract

Human movements have raised broad attention, and many models have been developed to reproduce them. However, most studies focus on reproducing the statistical properties of human mobility, such as the travel distance and the visiting frequency. In this paper, a two-step Markov Chain model is proposed to generate daily human movements, and spatial and spatiotemporal attributes of reproduced mobility are examined. In the first step, people’s statuses in the next time slot are conditioned on their previous travel patterns; and in the second step, individual location in such a slot is probabilistically determined based on his/her status. Our model successfully reproduces the spatial and spatiotemporal characteristics of human daily movements, and the result indicates that people’s future statuses can be inferred based on travel patterns they made, regardless of exactly where they have traveled, and when trips happen. We also revisit the energy concept, and show that the energy expenditure is stable over years. This idea is further used to predict the proportion of long-distance trips for each year, which gives insights into the probabilities of statuses in the next time slot. Finally, we interpret the constant energy expenditure as the constant ‘cost’ over years.