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

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.

Data Mining and Knowledge Discovery, 2018

References in corpus (12)

• Understanding individual human mobility patterns
• The scaling laws of human travel
• Human Mobility: Models and Applications
• Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions
• Understanding Road Usage Patterns in Urban Areas
• Gravity model in the Korean highway
• Systematic comparison of trip distribution laws and models
• The Dynamics of a Mobile Phone Network
• The Death and Life of Great Italian Cities: A Mobile Phone Data Perspective
• An analytical framework to nowcast well-being using mobile phone data
• The Effect of Recency to Human Mobility
• Human diffusion and city influence

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• An Indoor Crowd Movement Trajectory Benchmark Dataset
• Towards Standardized Mobility Reports with User-Level Privacy
• Maximum Entropy Approach for the Prediction of Urban Mobility Patterns
• Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information
• Modelling Human Mobility considering Spatial,Temporal and Social Dimensions
• Methodology for Mining, Discovering and Analyzing Semantic Human Mobility Behaviors