[Paper Review] Modelling individual routines and spatio-temporal trajectories in human mobility.

Luca Pappalardo, Filippo Simini|arXiv (Cornell University)|Jul 16, 2016

Human Mobility and Location-Based Analysis72 references4 citations

TL;DR

DITRAS is a two-step framework that generates realistic human mobility trajectories by first modeling individual mobility diaries using a Markov process to capture routine adherence and disruption, then translating these diaries into spatio-temporal trajectories via preferential exploration and return mechanisms. It accurately reproduces key statistical properties of real-world mobility data, outperforming existing synthetic models in fidelity.

ABSTRACT

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. In this article we present 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.

Motivation & Objective

• To address the limitation of existing mobility models in capturing both routine behavior and spontaneous deviations in human mobility.
• To develop a generative model that realistically simulates individual spatio-temporal trajectories over time.
• To enable accurate simulation of mobility for applications such as mobile ad hoc networks and urban ecosystem modeling.
• To integrate both habitual patterns and unpredictable behavioral changes in a single, unified framework.

Proposed method

• A Markov model generates mobility diaries that encode daily routines and the probability of breaking them during unpredictable periods.
• The diary generation process models transitions between activity types and locations based on individual behavioral tendencies.
• A preferential exploration and return mechanism translates the mobility diary into actual spatio-temporal trajectories.
• The trajectory model assigns movement probabilities based on familiarity and exploration tendencies, mimicking real human movement patterns.
• The framework combines diary-based behavioral modeling with spatial movement rules to simulate realistic mobility patterns.
• The model is calibrated and validated using real human mobility data, with comparisons to synthetic data from other mobility models.

Experimental results

Research questions

• RQ1 How well can a diary-based model capture the balance between routine behavior and spontaneous deviations in human mobility?
• RQ2 To what extent does the DITRAS framework reproduce the statistical properties of real human mobility trajectories?
• RQ3 Can a two-step approach—diary generation followed by trajectory translation—produce more realistic mobility patterns than existing models?
• RQ4 How do preferential exploration and return mechanisms improve the realism of simulated mobility trajectories?

Key findings

• DITRAS successfully captures the dual nature of human mobility: consistent daily routines and occasional deviations during unpredictable periods.
• The framework reproduces key statistical properties of real mobility data, such as visitation frequency and location distribution, with high accuracy.
• Compared to other synthetic mobility models, DITRAS generates trajectories that more closely match empirical data in terms of spatial and temporal patterns.
• The use of a Markov-based diary model enables realistic modeling of habit formation and disruption, enhancing behavioral fidelity.
• The preferential exploration and return mechanism effectively simulates natural human movement tendencies, improving trajectory realism.
• The two-step architecture of DITRAS allows for modular and scalable simulation of individual mobility across diverse scenarios.