DUGET: Leveraging Machine Learning for Dynamic User Grouping and Evolution Tracking in Public Transit Systems

This work aims to explore the use of machine learning techniques, particularly clustering and cluster evolution tracking, to analyze travel patterns in public transportation in a city and provide valuable insights for urban transit planning and optimization. Clustering involves identifying and grouping similar objects, such as passengers with different ticket types, and distinguishing them from dissimilar objects in other groups. Over time, groups can change, so tracking this change can provide more detailed and valuable insights than analyzing data in aggregates. Clustering and cluster evolution tracking can reveal groups of passengers that are more or less affected by changes such as seasonality or fare increases. We propose a framework called DUGET (Dynamic User Grouping and Evolution Tracking), which clusters anonymized users based on their ticket choices and temporal travel patterns using a multi-step approach. The clusters are then tracked over time using Jaccard similarity based on memberships, allowing for the analysis and visualization of changes. Our experiments using a real-world public transportation dataset collected in Stockholm, Sweden, show the feasibility of tracking change over time in public transportation by examining passenger behavior as a temporal aggregate. The framework we propose is generalizable and can be used for future projects to understand trends in groups of objects.