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Smart city article details
| Title | Leveraging Crowdsourcing For Mapping Mobility Restrictions In Data-Limited Regions |
|---|---|
| ID_Doc | 35052 |
| Authors | Aburas H.; Shahrour I.; Sadek M. |
| Year | 2024 |
| Published | Smart Cities, 7, 5 |
| DOI | http://dx.doi.org/10.3390/smartcities7050100 |
| Abstract | Highlights: What are the main findings? Developed a novel methodology for real-time mapping of mobility restrictions using spatial crowdsourcing and Telegram in data-limited regions. Achieved validation rates (67–100%) and precision (73%) for traffic event data collected and analyzed through this methodology. What is the implication of the main findings? Enhanced traffic management and informed decision-making in regions with limited traditional data collection infrastructure. Provided a scalable model that can be applied to other regions with similar data limitations, contributing to the field of smart city technologies. 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. © 2024 by the authors. |
| Author Keywords | ArcGIS; clustering; crowdsourcing; mapping; mobility restrictions; NLP; Telegram |
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