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Are Crowded Events Forecastable from Promotional Announcements with Large Language Models?

Published: 22 November 2024 Publication History

Abstract

Forecasting the number of visitors at a public event, termed event crowd forecasting (ECF), has recently garnered attention due to its social significance. Although existing ECF methods have pioneered successful feature design by considering event contents with contexts (e.g., weather, type of day, time), their scalability across different event types is limited due to the necessity of costly feature engineering. To address this issue, we propose a novel ECF framework, named EventOutlook. Based on our observation of various events, online event announcements indicate the factors that induce crowded events. Thus, we incorporate event announcements into ECF methods. To handle such unstructured data, which have no unified format among events, we leverage large language models (LLM) to extract crowding factors and embed them into an LLM-driven crowding-indicator feature (LCIF). Empirical experiments with real-world event data show that EventOutlook significantly improved ECF performance compared to state-of-the-art methods.

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cover image ACM Conferences
SIGSPATIAL '24: Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems
October 2024
743 pages
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 November 2024

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Author Tags

  1. Crowd Forecasting
  2. Event Announcement
  3. Event Attendance
  4. Event Crowd
  5. Large Language Model
  6. Mobility Logs

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SIGSPATIAL '24
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SIGSPATIAL '24 Paper Acceptance Rate 37 of 122 submissions, 30%;
Overall Acceptance Rate 257 of 1,238 submissions, 21%

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