Forecasting Lifespan of Crowded Events With Acoustic Synthesis-Inspired Segmental Long Short-Term Memory

Abstract

Forecasting crowd congestion is crucial for ensuring comfortable mobility and public safety. Existing methods forecast crowding by capturing the increase in planned visits, which facilitates the methods in estimating the start of crowding. However, forecasting the change in the degree of crowding until the end is challenging owing to the lack of visitors’ return plans and the deviation of visitor movements from preannounced event schedules. To address this issue, this study developed a novel framework for forecasting the start of crowding and its change over time (termed the lifespan of crowded events (LCE)). Based on the concept that event purposes influence the crowding patterns, our framework models these patterns according to the event purposes. Inspired by the acoustic synthesis that can successfully model the change in the sound volume for each instrument, we extended a canonical long short-term memory (LSTM) model with the concept of ADSR envelope, wherein the sound (crowd) volume changes can be represented within simple state transitions. The proposed versatile acoustic tri-state envelope for segmental LSTM, namely VATES, is evaluated on two datasets: synthetic and real-world mobility datasets. The results demonstrate that VATES can forecast crowding patterns with a 24.3% performance improvement, and precisely predict the start and end times of crowding, thereby improving by 6.6% and 26.1% respectively. We believe that our method enhances urban safety and mobility in crowded events, contributing to smarter city management.

Keywords

• Crowd forecasting
• urban computing
• ADSR envelope
• acoustic synthesis
• time series forecasting