{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/data-driven-generation-of-spatio-temporal","title":"Data-driven generation of spatio-temporal routines in human mobility","arxiv_id":"1607.05952","date":"2016-07-16","proceeding":null,"authors":["Luca Pappalardo","Filippo Simini"],"abstract":"The generation of realistic spatio-temporal trajectories of human mobility is\nof fundamental importance in a wide range of applications, such as the\ndeveloping of protocols for mobile ad-hoc networks or what-if analysis in urban\necosystems. Current generative algorithms fail in accurately reproducing the\nindividuals' recurrent schedules and at the same time in accounting for the\npossibility that individuals may break the routine during periods of variable\nduration. In this article we present DITRAS (DIary-based TRAjectory Simulator),\na framework to simulate the spatio-temporal patterns of human mobility. DITRAS\noperates in two steps: the generation of a mobility diary and the translation\nof the mobility diary into a mobility trajectory. We propose a data-driven\nalgorithm which constructs a diary generator from real data, capturing the\ntendency of individuals to follow or break their routine. We also propose a\ntrajectory generator based on the concept of preferential exploration and\npreferential return. We instantiate DITRAS with the proposed diary and\ntrajectory generators and compare the resulting algorithm with real data and\nsynthetic data produced by other generative algorithms, built by instantiating\nDITRAS with several combinations of diary and trajectory generators. We show\nthat the proposed algorithm reproduces the statistical properties of real\ntrajectories in the most accurate way, making a step forward the understanding\nof the origin of the spatio-temporal patterns of human mobility.","url_abs":"http://arxiv.org/abs/1607.05952v3","url_pdf":"http://arxiv.org/pdf/1607.05952v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"data-driven-generation-of-spatio-temporal","repo_url":"https://github.com/jonpappalord/DITRAS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1607.05952","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}