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Enhancing Crowd Flow Prediction in Various Spatial and Temporal Granularities

Authors:

Massimiliano Luca,

Luca PappalardoAuthors Info & Claims

WWW '22: Companion Proceedings of the Web Conference 2022

Pages 1251 - 1259

https://doi.org/10.1145/3487553.3524851

Published: 16 August 2022 Publication History

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Abstract

The diffusion of the Internet of Things allows nowadays to sense human mobility in great detail, fostering human mobility studies and their applications in various contexts, from traffic management to public security and computational epidemiology. A mobility task that is becoming prominent is crowd flow prediction, i.e., forecasting aggregated incoming and outgoing flows in the locations of a geographic region. Although several deep learning approaches have been proposed to solve this problem, their usage is limited to specific types of spatial tessellations and cannot provide sufficient explanations of their predictions. We propose CrowdNet, a solution to crowd flow prediction based on graph convolutional networks. Compared with state-of-the-art solutions, CrowdNet can be used with regions of irregular shapes and provide meaningful explanations of the predicted crowd flows. We conduct experiments on public data varying the spatio-temporal granularity of crowd flows to show the superiority of our model with respect to existing methods, and we investigate CrowdNet’s reliability to missing or noisy input data. Our model is a step forward in the design of reliable deep learning models to predict and explain human displacements in urban environments.

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Cited By

Tang CDong LGuo HWang XChen XDong QLiu Y(2024)Downscaling spatial interaction with socioeconomic attributesEPJ Data Science10.1140/epjds/s13688-024-00487-w13:1Online publication date: 5-Jul-2024
https://doi.org/10.1140/epjds/s13688-024-00487-w
Wen CHe HQian YXie YWang W(2024)Fourier feature decorrelation based sample attention for dense crowd localizationNeural Networks10.1016/j.neunet.2024.106131(106131)Online publication date: Jan-2024
https://doi.org/10.1016/j.neunet.2024.106131
Wang XBai WMeng ZXin BGao RLv X(2023)The Prediction of Flow in Railway Station Based on RRC-STGCNIEEE Access10.1109/ACCESS.2023.333428011(131128-131139)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3334280

Index Terms

Enhancing Crowd Flow Prediction in Various Spatial and Temporal Granularities
1. Applied computing
  1. Operations research
    1. Transportation
2. Computing methodologies
  1. Artificial intelligence
  2. Machine learning

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cover image ACM Conferences

WWW '22: Companion Proceedings of the Web Conference 2022

April 2022

1338 pages

ISBN:9781450391306

DOI:10.1145/3487553

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Lionel Médini
Université Lyon 1, France
,
Ivan Herman
W3C / retired

Copyright © 2022 ACM.

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Published: 16 August 2022

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WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

Virtual Event, Lyon, France

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Cited By

Tang CDong LGuo HWang XChen XDong QLiu Y(2024)Downscaling spatial interaction with socioeconomic attributesEPJ Data Science10.1140/epjds/s13688-024-00487-w13:1Online publication date: 5-Jul-2024
https://doi.org/10.1140/epjds/s13688-024-00487-w
Wen CHe HQian YXie YWang W(2024)Fourier feature decorrelation based sample attention for dense crowd localizationNeural Networks10.1016/j.neunet.2024.106131(106131)Online publication date: Jan-2024
https://doi.org/10.1016/j.neunet.2024.106131
Wang XBai WMeng ZXin BGao RLv X(2023)The Prediction of Flow in Railway Station Based on RRC-STGCNIEEE Access10.1109/ACCESS.2023.333428011(131128-131139)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3334280

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