Predictive modeling of non-daily pedestrian dynamics and crowd congestion around high-density Points of Interest (POIs) using synthetic mobility data
実施中
中居 楓子
Understanding the mechanisms behind destination choice for non-daily travel behavior is critical for addressing the growing challenges of localized overtourism, urban congestion, and the uneven distribution of pedestrian flows. This research investigates the complex spatio-temporal dynamics of human mobility within major Japanese metropolitan areas, specifically Tokyo, Kyoto, Osaka, and Chiba, focusing on non-daily activities such as leisure, sightseeing, and shopping around commercial and recreational Points of Interest (POIs). Recognizing the severe strain that high-density crowd accumulation places on urban infrastructure, public transport systems, and the quality of life for local residents, this study utilizes the Nationwide Synthetic Human Mobility Dataset (Pseudo-PFLOW) to extract, reconstruct, and evaluate granular crowd distribution matrices. By exploring these synthetic yet highly representative trajectories, we aim to uncover the underlying patterns driving spontaneous and non-routine destination choices.
変更のために新しい申請を保存します。 This will save a new application on the system for a modification.
申請中の研究者は表示されません。 / Pending researchers are not shown.
Robert Olszewski / Warsaw University of Technology
申請中のデータセットは表示されません。 / Pending datasets are not shown.
擬似人流・人口属性データ 東京都データセット
擬似人流・人口属性データ 京都府データセット
擬似人流・人口属性データ 大阪府データセット
擬似人流・人口属性データ 奈良県データセット
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