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Replicating urban dynamics by generating human-like agents from smartphone GPS data

Authors:

Yanbo Pang,

Kota Tsubouchi,

Takahiro Yabe,

Yoshihide SekimotoAuthors Info & Claims

SIGSPATIAL '18: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 440 - 443

https://doi.org/10.1145/3274895.3274935

Published: 06 November 2018 Publication History

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Abstract

This paper is the first work to replicate and simulate urban dynamics by learning individuals' decision-making processes and creating human-like agents from GPS data. We develop a novel agent model by learning from historical data via reinforcement learning techniques. We test our methodology in different scenarios at the citywide level using real world smartphone GPS data. Simulation results show that our agents can successfully learn and generate human-like travel activities. Furthermore, the performance of synthetic urban dynamics significantly outperforms existing methods.

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Cited By

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Barreras FWatts D(2024)The exciting potential and daunting challenge of using GPS human-mobility data for epidemic modelingNature Computational Science10.1038/s43588-024-00637-04:6(398-411)Online publication date: 19-Jun-2024
https://doi.org/10.1038/s43588-024-00637-0
Kapp AHansmeyer JMihaljević H(2023)Generative Models for Synthetic Urban Mobility Data: A Systematic Literature ReviewACM Computing Surveys10.1145/361022456:4(1-37)Online publication date: 10-Nov-2023
https://dl.acm.org/doi/10.1145/3610224
Jiang WMeng YWang P(2023)An insightful metric for evaluating perceived benefits from water quality enhancement in waterscape parks: A behavioral analysis approachEcological Indicators10.1016/j.ecolind.2023.111292157(111292)Online publication date: Dec-2023
https://doi.org/10.1016/j.ecolind.2023.111292
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Replicating urban dynamics by generating human-like agents from smartphone GPS data

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Information & Contributors

Information

Published In

SIGSPATIAL '18: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2018

655 pages

ISBN:9781450358897

DOI:10.1145/3274895

General Chairs:
Farnoush Banaei-Kashani
University of Colorado, Denver
,
Erik Hoel
Esri
,
Program Chairs:
Ralf Hartmut Güting
FernUniversität in Hagen, Germany
,
Roberto Tamassia
Brown University
,
Li Xiong
Emory University

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2018

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SIGSPATIAL '18

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SIGSPATIAL

SIGSPATIAL '18: 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 6 - 9, 2018

Washington, Seattle

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SIGSPATIAL '18 Paper Acceptance Rate 30 of 150 submissions, 20%;

Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Cited By

View all

Barreras FWatts D(2024)The exciting potential and daunting challenge of using GPS human-mobility data for epidemic modelingNature Computational Science10.1038/s43588-024-00637-04:6(398-411)Online publication date: 19-Jun-2024
https://doi.org/10.1038/s43588-024-00637-0
Kapp AHansmeyer JMihaljević H(2023)Generative Models for Synthetic Urban Mobility Data: A Systematic Literature ReviewACM Computing Surveys10.1145/361022456:4(1-37)Online publication date: 10-Nov-2023
https://dl.acm.org/doi/10.1145/3610224
Jiang WMeng YWang P(2023)An insightful metric for evaluating perceived benefits from water quality enhancement in waterscape parks: A behavioral analysis approachEcological Indicators10.1016/j.ecolind.2023.111292157(111292)Online publication date: Dec-2023
https://doi.org/10.1016/j.ecolind.2023.111292
Tamura NUrano KAoki SYonezawa TKawaguchi N(2022)Synthetic People Flow: Privacy-Preserving Mobility Modeling from Large-Scale Location Data in Urban AreasMobile and Ubiquitous Systems: Computing, Networking and Services10.1007/978-3-030-94822-1_36(553-567)Online publication date: 8-Feb-2022
https://doi.org/10.1007/978-3-030-94822-1_36
Pan MHuang WLi YZhou XLiu ZBao JZheng YLuo J(2020)Is Reinforcement Learning the Choice of Human Learners?Proceedings of the 28th International Conference on Advances in Geographic Information Systems10.1145/3397536.3422246(357-366)Online publication date: 3-Nov-2020
https://dl.acm.org/doi/10.1145/3397536.3422246
Pang YTsubouchi KYabe TSekimoto Y(2020)Intercity Simulation of Human Mobility at Rare Events via Reinforcement LearningProceedings of the 28th International Conference on Advances in Geographic Information Systems10.1145/3397536.3422244(293-302)Online publication date: 3-Nov-2020
https://dl.acm.org/doi/10.1145/3397536.3422244

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