Data-driven generation of spatio-temporal routines in human mobility

TL;DR

This paper introduces DITRAS, a data-driven framework for generating realistic human mobility trajectories that accurately reproduce recurrent schedules and routine-breaking behaviors, improving upon existing models.

Contribution

The paper presents DITRAS, a novel two-step, data-driven framework combining a diary generator and a trajectory generator to simulate human mobility patterns more accurately.

Findings

DITRAS outperforms existing algorithms in reproducing real trajectory statistics.

The diary generator effectively captures routine and non-routine behaviors.

The trajectory generator models preferential exploration and return behaviors.

Abstract

The generation of realistic spatio-temporal trajectories of human mobility is of fundamental importance in a wide range of applications, such as the developing of protocols for mobile ad-hoc networks or what-if analysis in urban ecosystems. Current generative algorithms fail in accurately reproducing the individuals' recurrent schedules and at the same time in accounting for the possibility that individuals may break the routine during periods of variable duration. In this article we present DITRAS (DIary-based TRAjectory Simulator), a framework to simulate the spatio-temporal patterns of human mobility. DITRAS operates in two steps: the generation of a mobility diary and the translation of the mobility diary into a mobility trajectory. We propose a data-driven algorithm which constructs a diary generator from real data, capturing the tendency of individuals to follow or break their routine. We also propose a trajectory generator based on the concept of preferential exploration and preferential return. We instantiate DITRAS with the proposed diary and trajectory generators and compare the resulting algorithm with real data and synthetic data produced by other generative algorithms, built by instantiating DITRAS with several combinations of diary and trajectory generators. We show that the proposed algorithm reproduces the statistical properties of real trajectories in the most accurate way, making a step forward the understanding of the origin of the spatio-temporal patterns of human mobility.