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Visualizing Congestion at Mass-Gathering Events with Proximity-Based Networks | IEEE Conference Publication | IEEE Xplore

Visualizing Congestion at Mass-Gathering Events with Proximity-Based Networks


Abstract:

Infectious diseases are typically transmitted through close contact with infected persons. The effective management of overcrowding is a crucial issue for events with a l...Show More

Abstract:

Infectious diseases are typically transmitted through close contact with infected persons. The effective management of overcrowding is a crucial issue for events with a large number of attendees. Since the COVID-19 outbreak, analyzing people flow to recognize pedestrian behavior and walking patterns have been attracted studies. Visualizing crowded high-risk situations for infection at large gatherings is a complex task. It requires an approach that can effectively represent both spatial and temporal features while ensuring that the visibility of walking paths is not significantly compromised. To address these issues, we propose a novel approach for visualizing proximity as a network that represents the distance relationship between pedestrians. We developed the visualization system linking three components: Proximity Network, the walking paths of selected pedestrians from the network, and the temporal statistics of pedestrian traffic. Users of this system can freely select a group of pedestrians from Proximity Network and observe the paths of the selected pedestrians. This procedure enables better visibility of walking paths and an understanding of their spatio-temporal characteristics because only a smaller number of paths are drawn. This paper presents our case study of the proposed method for visualizing pedestrian proximity using real-world people flow data collected at an event site.
Date of Conference: 25-28 July 2023
Date Added to IEEE Xplore: 06 November 2023
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Conference Location: Tampere, Finland

References

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