A Data-Driven Platform for Visualizing and Analyzing Public Transit Schedule Deviations

This paper presents a methodology and an associated platform for classifying and visualizing schedule deviations in public transit systems. Applied to the Montreal public transit network, the work offers two main contributions: (1) a methodology integrating real-time transit data processing (GTFS-RT), systematic/stochastic deviation classification, and multi-resolution spatial analysis using the Hexagonal Hierarchical Spatial Index (H3) system (Resolutions 9 and 10); (2) an interactive visualization platform employing KeplerGL, built upon this methodology, enabling dynamic exploration of spatio-temporal deviation patterns. The platform reveals significant schedule deviations across Montreal's network. While preliminary, the visualized patterns provide data-driven evidence to generate actionable hypotheses and guide further investigation for transit optimization and urban planning, particularly in identifying delay-prone areas and understanding spatial delay propagation.