Urban Mobility and Demand Dynamics: A Telemetry and Ticketing Baseline for Niteroi's Bus Network
The public transportation networks of rapidly expanding urban centers confront a complex task in balancing operational efficiency with dynamic commuter needs. In response, data-driven approaches leveraging multi-perspective datasets offer critical insights into a city's systemic demand. In this work, we utilize the NetMob 2026 Data Challenge dataset for the city of Niteroi, Brazil, to investigate the structural dynamics of the municipal bus network. Specifically, our research framework is driven by three interconnected urban mobility objectives: first, analyzing urban movement patterns to determine whether passenger boarding logs trace periodic, routine, and predictable movement patterns across distinct urban zones; second, detecting operational anomalies and potential bottlenecks by analyzing vehicle trajectory profiles via GPS telemetry to identify both scheduled and unscheduled stops; and third, developing and evaluating a supervised learning framework capable of predicting continuous bus trip durations.