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arXiv · 2601.14570

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

Xiaojie Yang·Dizhi Huang·Hangli Ge·Masahiro Sano·Takeaki Ohdake·Kazuma Hatano·Noboru Koshizuka
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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.

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Xiaojie Yang, Dizhi Huang, Hangli Ge, Masahiro Sano, Takeaki Ohdake, Kazuma Hatano, Noboru Koshizuka. 2026-01-21. Place with Intention: An Empirical Attendance Predictive Study of Expo 2025 Osaka, Kansai, Japan. https://arxiv.org/abs/2601.14570

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