Place with Intention: An Empirical Attendance Predictive Study of Expo 2025 Osaka, Kansai, Japan

2026-01-21

AI Summary

This study addresses the challenge of inaccurate visitor flow forecasting for large-scale international events—such as Expo 2025 Osaka—where scarce historical data necessitates reliance on heterogeneous external sources. To circumvent explicit multi-source data fusion, the authors propose a Transformer-based forecasting framework that innovatively leverages dynamic ticket booking records as a proxy for visitor attendance intent, thereby implicitly capturing the influence of external factors like weather and promotions. The model employs an encoder–decoder architecture enhanced with inverse style embedding and an adaptive fusion module to separately model dual-channel pedestrian flows at the east and west entrances. Experimental results demonstrate that gate-specific modeling significantly outperforms aggregate modeling in short- to medium-term prediction, while ablation studies confirm the efficacy of each architectural component and validate ticket booking dynamics as a reliable signal for crowd flow forecasting.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Machine Learning: Time-Series/Data Streams
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Accurate forecasting of daily attendance is vital for managing transportation, crowd flows, and services at large-scale international events such as Expo 2025 Osaka, Kansai, Japan. However, existing approaches often rely on multi-source external data (such as weather, traffic, and social media) to improve accuracy, which can lead to unreliable results when historical data are insufficient. To address these challenges, we propose a Transformer-based framework that leverages reservation dynamics, i.e., ticket bookings and subsequent updates within a time window, as a proxy for visitors'attendance intentions, under the assumption that such intentions are eventually reflected in reservation patterns. This design avoids the complexity of multi-source integration while still capturing external influences like weather and promotions implicitly embedded in reservation dynamics. We construct a dataset combining entrance records and reservation dynamics and evaluate the model under both single-channel (total attendance) and two-channel (separated by East and West gates) settings. Results show that separately modeling East and West gates consistently improves accuracy, particularly for short- and medium-term horizons. Ablation studies further confirm the importance of the encoder-decoder structure, inverse-style embedding, and adaptive fusion module. Overall, our findings indicate that reservation dynamics offer a practical and informative foundation for attendance forecasting in large-scale international events.

Problem

• attendance prediction
• Expo 2025
• reservation dynamics
• large-scale events
• forecasting

Innovation

• Transformer-based forecasting
• reservation dynamics
• attendance prediction
• intention proxy
• adaptive fusion module