1 citations · 1 across the 4 of their papers we have counts for
5 papers
Fine-tuning LLMs for Tourist Trajectory Prediction using Field Experiment Data
Tatsuya Amano, Hirozumi Yamaguchi
Evaluating mobility interventions at tourist destinations requires predicting visitor behavior under varying conditions. Traditional methods struggle because tourist decisions depe…
Distilling Aggregate Mobility Statistics into a Language Model Policy for Post-Event Crowd Simulation
Tatsuya Amano, Hirozumi Yamaguchi
Pedestrian simulators need a behaviour rule for every agent, but privacy usually limits the data for setting one to aggregate statistics, namely zone-level device counts and origin…
Human-Flow Digital Twin for Predicting the Effects of Mobility Introduction on Visitor Circulation
Chiharu Shima, Haruki Yonekura, Fukuharu Tanaka +2
We propose a framework for predicting the effects of mobility introduction measures using a human-flow digital twin. This digital twin incorporates a multi-agent simulator that can…
MobText-SISA: Efficient Machine Unlearning for Mobility Logs with Spatio-Temporal and Natural-Language Data
Haruki Yonekura, Ren Ozeki, Tatsuya Amano +2
Modern mobility platforms have stored vast streams of GPS trajectories, temporal metadata, free-form textual notes, and other unstructured data. Privacy statutes such as the GDPR r…
Privacy-preserving Pedestrian Tracking using Distributed 3D LiDARs
Masakazu Ohno, Riki Ukyo, Tatsuya Amano +2
The growing demand for intelligent environments unleashes an extraordinary cycle of privacy-aware applications that makes individuals' life more comfortable and safe. Examples of t…