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

TL;DR

This paper introduces a Transformer-based model that predicts attendance at Expo 2025 using reservation data as a proxy for visitor intentions, effectively capturing external influences without multi-source data reliance.

Contribution

The study presents a novel reservation dynamics-based framework for attendance forecasting, avoiding complex multi-source data integration and demonstrating improved accuracy with gate-specific modeling.

Findings

Separate modeling of East and West gates improves accuracy.

Reservation dynamics effectively reflect external influences like weather and promotions.

The proposed model outperforms baseline approaches in short- and medium-term forecasts.

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.