Stigmergy-based modeling to discover urban activity patterns from positioning data
This work addresses the challenge of analyzing complex urban crowd dynamics for urban planners and researchers, though it appears incremental by applying an existing computational paradigm to a specific dataset.
The paper tackled the problem of discovering urban activity patterns from positioning data by using a stigmergy-based approach to model crowd dynamics, resulting in the identification of hotspots and clustering of days to reveal unexpected patterns, as demonstrated with taxi traces in New York City in 2015.
Positioning data offer a remarkable source of information to analyze crowds urban dynamics. However, discovering urban activity patterns from the emergent behavior of crowds involves complex system modeling. An alternative approach is to adopt computational techniques belonging to the emergent paradigm, which enables self-organization of data and allows adaptive analysis. Specifically, our approach is based on stigmergy. By using stigmergy each sample position is associated with a digital pheromone deposit, which progressively evaporates and aggregates with other deposits according to their spatiotemporal proximity. Based on this principle, we exploit positioning data to identify high density areas (hotspots) and characterize their activity over time. This characterization allows the comparison of dynamics occurring in different days, providing a similarity measure exploitable by clustering techniques. Thus, we cluster days according to their activity behavior, discovering unexpected urban activity patterns. As a case study, we analyze taxi traces in New York City during 2015.