[2A5-GS-10] AI application: Traffic

[2A5-GS-10-03] Large Language Model for Urban People Flow Analysis and Its Application to Synthetic Data Generation

〇Yanbo Pang¹, Kunyi Zhang¹, Yurong Zhang¹, Yoshihide Sekimoto¹ (1. The University of Tokyo)

Keywords:Large Language Model, Human Mobility Analysis, Pseudo People Flow Data, GPT

In recent years, with the advancement of smart cities and smart mobility, there has been an increasing need to efficiently and cost-effectively understand people's behaviors in detail, despite the constraints imposed by the protection of personal information. This study aims to effectively learn and predict the spatial patterns of people's daily activities by utilizing trip survey data collected from approximately 6 million individuals across 20 urban areas in Japan, employing large-scale language models. We adopt the Transformer architecture, which has demonstrated high performance in various tasks, and leverage unsupervised learning methods for robust transferability. This approach aims to integrate demographic information with trip data to generate more comprehensive and accurate synthetic human flow data. Furthermore, we explore how to utilize census-based behavioral data for the development of human flow models in developing countries, thereby opening new possibilities in this field."