Leveraging Crowdsourcing for Mapping ...
Document type :
Article dans une revue scientifique: Article original
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Title :
Leveraging Crowdsourcing for Mapping Mobility Restrictions in Data-Limited Regions
Author(s) :
Aburas, Hala [Auteur]
Shahrour, Isam [Auteur]
Laboratoire de Génie Civil et Géo-Environnement (LGCgE) - ULR 4515 [LGCgE]
Sadek, Marwan [Auteur]
Laboratoire de Génie Civil et Géo-Environnement (LGCgE) - ULR 4515 [LGCgE]
Shahrour, Isam [Auteur]

Laboratoire de Génie Civil et Géo-Environnement (LGCgE) - ULR 4515 [LGCgE]
Sadek, Marwan [Auteur]

Laboratoire de Génie Civil et Géo-Environnement (LGCgE) - ULR 4515 [LGCgE]
Journal title :
Smart Cities
Pages :
2572-2593
Publisher :
MDPI
Publication date :
2024-09-07
ISSN :
2624-6511
HAL domain(s) :
Sciences de l'ingénieur [physics]
English abstract : [en]
This paper introduces a novel methodology for the real-time mapping of mobility restrictions, utilizing spatial crowdsourcing and Telegram as a traffic event data source. This approach is efficient in regions suffering ...
Show more >This paper introduces a novel methodology for the real-time mapping of mobility restrictions, utilizing spatial crowdsourcing and Telegram as a traffic event data source. This approach is efficient in regions suffering from limitations in traditional data-capturing devices. The methodology employs ArcGIS Online (AGOL) for data collection, storage, and analysis, and develops a 3W (what, where, when) model for analyzing mined Arabic text from Telegram. Data quality validation methods, including spatial clustering, cross-referencing, and ground-truth methods, support the reliability of this approach. Applied to the Palestinian territory, the proposed methodology ensures the accurate, timely, and comprehensive mapping of traffic events, including checkpoints, road gates, settler violence, and traffic congestion. The validation results indicate that using spatial crowdsourcing to report restrictions yields promising validation rates ranging from 67% to 100%. Additionally, the developed methodology utilizing Telegram achieves a precision value of 73%. These results demonstrate that this methodology constitutes a promising solution, enhancing traffic management and informed decision-making, and providing a scalable model for regions with limited traditional data collection infrastructure.Show less >
Show more >This paper introduces a novel methodology for the real-time mapping of mobility restrictions, utilizing spatial crowdsourcing and Telegram as a traffic event data source. This approach is efficient in regions suffering from limitations in traditional data-capturing devices. The methodology employs ArcGIS Online (AGOL) for data collection, storage, and analysis, and develops a 3W (what, where, when) model for analyzing mined Arabic text from Telegram. Data quality validation methods, including spatial clustering, cross-referencing, and ground-truth methods, support the reliability of this approach. Applied to the Palestinian territory, the proposed methodology ensures the accurate, timely, and comprehensive mapping of traffic events, including checkpoints, road gates, settler violence, and traffic congestion. The validation results indicate that using spatial crowdsourcing to report restrictions yields promising validation rates ranging from 67% to 100%. Additionally, the developed methodology utilizing Telegram achieves a precision value of 73%. These results demonstrate that this methodology constitutes a promising solution, enhancing traffic management and informed decision-making, and providing a scalable model for regions with limited traditional data collection infrastructure.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Source :
Submission date :
2025-02-25T14:01:31Z
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