Replicating Urban Dynamics by Generating Human-like Agents from Smartphone GPS Data
Yanbo Pang (UTokyo), Kota Tsubouchi, Takahiro Yabe (UTokyo), Yoshihide Sekimoto (UTokyo)
The ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2018 (ACM SIGSPATIAL 2018), 2018
Machine Learning Data Science
- This paper is the firstwork 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-based simulation on the basis of 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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