プロジェクト情報（概要） / Project information (abstract)
基本情報 / Basic Informations
- 研究題目
Research Title 強化学習に基づく人の流れのシミュレーションのためのエージェントモデルの構築とその応用
Development of an Agent Model for People Flow Simulation Based on Reinforcement Learning Approach
- 状態
Status -
完了
Completed projects
- 研究番号
Research Number - 1033
- 研究代表者
PI - 龐岩博 / 東京大学 空間情報科学研究センター
- 事務担当者
Secretary - 龐岩博 / 東京大学 空間情報科学研究センター
- 受入CSIS教員
CSIS reception staff 小川 芳樹
Yoshiki OGAWA
- 研究内容
Abstract Understanding individual and crowd dynamics in urban environments are critical for numerous applications, such as urban planning, traffic forecasting, and location-based services. However, researchers have developed travel demand models to accomplish this task with active self-reported data or probe data which are very expensive and often in limited volume. In contrast, emerging data collection methods have enabled researchers to leverage machine learning techniques with a tremendous amount of passively collected mobility data for analyzing and forecasting people’s behaviors. In this study, we plan to develop a reinforcement learning-based approach for modeling and simulation of daily population movement using the Person Trip Survey data. Unlike traditional travel demand modeling approaches, our method focuses on the problem of inferring the spatio-temporal preferences of individuals from the observed trajectories and is based on inverse reinforcement learning (IRL) techniques. We apply the model to the Tokyo area and attempt to replicate a large amount of the population’s daily movement by incorporating with agent-based multi-modal traffic simulation technologies.
- 研究期間
Research Period - 2020-12-05 - 2022-03-31
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研究者 / Researchers
申請中の研究者は表示されません。 / Pending researchers are not shown.
龐岩博 / 東京大学 空間情報科学研究センター
研究成果 / Achievement
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