ArticleData mining and knowledge discovery2018
Data-driven generation of spatio-temporal routines in human mobility.
Article in Data mining and knowledge discovery, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Children's outdoor active mobility behaviour and neighbourhood safety: a systematic review in measurement methods and future research directions.International journal of health geographics · 2021Pooled it
- One rule does not fit all: Deviations from universality in human mobility modeling.PNAS nexus · 2026Article
- A maximum entropy approach for the modelling of car-sharing parking dynamics.Scientific reports · 2023Article
- A dataset to assess mobility changes in Chile following local quarantines.Scientific data · 2023Article
- Analysis and Implementation of Human Mobility Behavior Using Similarity Analysis Based on Co-Occurrence Matrix.Sensors (Basel, Switzerland) · 2022Article
- Generating mobility networks with generative adversarial networks.EPJ data science · 2022Article
- A Deep Gravity model for mobility flows generation.Nature communications · 2021Article
- Spatial Autoregressive Model for Estimation of Visitors' Dynamic Agglomeration Patterns Near Event Location.Sensors (Basel, Switzerland) · 2021Article
- The risk of re-identification remains high even in country-scale location datasets.Patterns (New York, N.Y.) · 2021Article
- Evaluation of home detection algorithms on mobile phone data using individual-level ground truth.EPJ data science · 2021Article
- Article
- A Spatio-Temporal Entropy-based Framework for the Detection of Trajectories Similarity.Entropy (Basel, Switzerland) · 2018Article
- Gravity and scaling laws of city to city migration.PloS one · 2018Article
- The Indoor Predictability of Human Mobility: Estimating Mobility with Smart Home Sensors.IEEE transactions on emerging topics in computingArticle
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.