Journal Article

FZJ-2021-01345

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png On the exploitation of GPS-based data for real-time visualisation of pedestrian dynamics in open environments

Alia, A. (First author)FZJ* ; Maree, M. (Corresponding author) ; Chraibi, M.FZJ*

2022
Taylor & Francis London

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Please use a persistent id in citations: http://hdl.handle.net/2128/31340  doi:10.1080/0144929X.2021.1896781

Abstract: Over the past few years, real-time visualisation of pedestrian dynamics has become more crucial to successfully organise and monitor open-crowded events. However, the process of collecting, efficiently handling and visualising a large volume of pedestrians' dynamic data in real time is challenging. This challenge becomes even more pronounced when pedestrians move in large-size, high-density, open and complex environments. In this article, we propose an efficient and accurate approach to acquire, process and visualise pedestrians' dynamic behaviour in real time. Our goal in this context is to produce GPS-based heat maps that assist event organisers as well as visitors in dynamically finding crowded spots using their smartphone devices. To validate our proposal, we have developed a prototype system for experimentally evaluating the quality of the proposed solution using real-world and simulation-based experimental datasets. The first phase of experiments was conducted in an open area with 37,000 square meters in Palestine. In the second phase, we have carried out a simulation for 5000 pedestrians to quantify the level of efficiency of the proposed system. We have utilised PHP scripting language to generate a larger-scale sample of randomly moving pedestrians across the same open area. A comparison with two well-known Web-based spatial data visualisation systems was conducted in the third phase. Findings indicate that the proposed approach

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Contributing Institute(s):

1. Zivile Sicherheitsforschung (IAS-7)

Research Program(s):

1. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)

Appears in the scientific report 2022

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Database coverage:
; ; Clarivate Analytics Master Journal List ; Current Contents - Social and Behavioral Sciences ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Social Sciences Citation Index ; Web of Science Core Collection

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