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Toward Ubiquitous Interaction-Attentive and Extreme-Aware Crowd Activity Level Prediction

Published: 19 November 2024 Publication History

Abstract

Accurate prediction of citywide crowd activity levels (CALs), i.e., the numbers of participants of citywide crowd activities under different venue categories at certain time and locations, is essential for the city management, the personal service applications, and the entrepreneurs in commercial strategic planning. Existing studies have not thoroughly taken into account the complex spatial and temporal interactions among different categories of CALs and their extreme occurrences, leading to lowered adaptivity and accuracy of their models. To address above concerns, we have proposed IE-CALP, a novel spatio-temporal Interactive attention-based and Extreme-aware model for Crowd Activity Level Prediction. The tasks of IE-CALP consist of (a) forecasting the spatial distributions of various CALs at different city regions (spatial CALs), and (b) predicting the number of participants per category of the CALs (categorical CALs). To realize above, we have designed a novel spatial CAL-POI interaction-attentive learning component in IE-CALP to model the spatial interactions across different CAL categories, as well as those among the spatial urban regions and CALs. In addition, IE-CALP incorporate the multi-level trends (e.g., daily and weekly levels of temporal granularity) of CALs through a multi-level temporal feature learning component. Furthermore, to enhance the model adaptivity to extreme CALs (e.g., during extreme urban events or weather conditions), we further take into account the extreme value theory and model the impacts of historical CALs upon the occurrences of extreme CALs. Extensive experiments upon a total of 738,715 CAL records and 246,660 POIs in New York City (NYC), Los Angeles (LA), and Tokyo have further validated the accuracy, adaptivity, and effectiveness of IE-CALP’s interaction-attentive and extreme-aware CAL predictions.

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  1. Toward Ubiquitous Interaction-Attentive and Extreme-Aware Crowd Activity Level Prediction

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    cover image ACM Transactions on Intelligent Systems and Technology
    ACM Transactions on Intelligent Systems and Technology  Volume 15, Issue 6
    December 2024
    727 pages
    EISSN:2157-6912
    DOI:10.1145/3613712
    • Editor:
    • Huan Liu
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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 19 November 2024
    Online AM: 29 July 2024
    Accepted: 20 July 2024
    Revised: 27 May 2024
    Received: 09 May 2022
    Published in TIST Volume 15, Issue 6

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    1. Crowd activity level
    2. spatio-temporal interaction
    3. points-of-interest (POI)
    4. extreme-aware prediction

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    • National Science Foundation (NSF)

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