Human mobility modelling is of fundamental importance in a wide range of applications, such as the developing of protocols for mobile ad hoc networks or for what-if analysis and simulation in urban ecosystems. Current generative models generally fail in accurately reproducing the individuals’ recurrent daily schedules and at the same time in accounting for the possibility that individuals may break the routine and modify their habits during periods of unpredictability of variable duration.

We propose DITRAS (DIary-based TRAjectory Simulator), a framework to simulate the spatio-temporal patterns of human mobility in a realistic way. DITRAS operates in two steps: the generation of a mobility diary and the translation of the mobility diary into a mobility trajectory. The mobility diary is constructed by a Markov model which captures the tendency of individuals to follow or break their routine. The mobility trajectory is produced by a model based on the concept of preferential exploration and preferential return. We compare DITRAS with real mobility data and synthetic data produced by other spatio-temporal mobility models and show that it reproduces the statistical properties of real trajectories in an accurate way.

Scientific article describing DITRAS: http://arxiv.org/abs/1607.05952

Python code to execute DITRAS (on GitHub):
https://github.com/jonpappalord/DITRAS