Using Social Media Data to Construct an Agent-Based Model in a Data-Scarce Environment: A Case Study of Alexandria, Egypt

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.