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

Yanbo Pang, Takahiro Yabe, Kota Tsubouchi, Yoshihide Sekimoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018
EditorsLi Xiong, Roberto Tamassia, Kashani Farnoush Banaei, Ralf Hartmut Guting, Erik Hoel
PublisherAssociation for Computing Machinery
Pages440-443
Number of pages4
ISBN (Electronic)9781450358897
DOIs
StatePublished - Nov 6 2018
Event26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018 - Seattle, United States
Duration: Nov 6 2018Nov 9 2018

Publication series

NameGIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

Other

Other26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018
Country/TerritoryUnited States
CitySeattle
Period11/6/1811/9/18

Keywords

  • Agent-based simulation
  • Human-like agent
  • Travel behavior modeling
  • Urban dynamics

ASJC Scopus subject areas

  • Earth-Surface Processes
  • Computer Science Applications
  • Modeling and Simulation
  • Computer Graphics and Computer-Aided Design
  • Information Systems

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