Using Social Media Data to Construct an Agent-Based Model in a Data-Scarce Environment

A Case Study of Alexandria, Egypt

Bibliographic Data

ID15318629
AuthorsMohamed Elgohary (0000-0003-2143-0600, Politecnico di Milano, corresponding author), Corneel Casier (0000-0002-7737-9237, Ghent University), Paola Pucci (0000-0003-1192-3890, Politecnico di Milano), Frank Witlox (0000-0002-8966-6823, Ghent University)
Year2025
Volume33
Issue1
Pages41-63
Publication date2025-11-03
Open AccessNo
TypeARTICLE
VenueJournal of Urban Technology (JOURNAL)
Journal identifiersISSN: 1063-0732 • E-ISSN: 1466-1853
PublisherTaylor & Francis (PUBLISHER • GB)
DOI10.1080/10630732.2025.2546785
OpenAlexW7103753376
LanguageEN
References cited20

In the transport and mobility sectors, agent-based models (ABMs) are known for their effectiveness in simulating complex scenarios; in data-scarce environments like the Global South, mobility data are fragmented or inaccessible, making ABM difficult to implement. To overcome these challenges, this article suggests employing open data sources like OpenStreetMap (OSM) and social media platforms (Meta). It presents a methodology incorporating social media data into ABMs through randomization techniques to estimate missing information, such as travel locations, modes, and activities. Using Alexandria, Egypt, as a case study, the approach shows a promising correlation with real-world traffic data, highlighting the potential of enhancing ABM accuracy with more comprehensive urban mobility data

Big data · Computational sociology · Construct (python library · Data collection · Social media · Urban computing · Human Mobility and Location-Based Analysis · Traffic Prediction and Management Techniques · Transportation and Mobility Innovations

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