The Value of Data in Municipalities
From Traditional Allocation to a Data- and AI-Driven Efficiency Model
From Traditional Allocation to a Data- and AI-Driven Efficiency Model
Optimizing subsidy distribution within municipal transit systems requires understanding service dynamics beyond raw mileage. This data analytics and artificial intelligence project focused on redesigning the budget allocation framework by uncovering actual operating costs and forecasting critical demand variables.
Multiple operational data sources were consolidated into a unified analytical model, pinpointing reporting discrepancies across bus lines. The preliminary analysis highlighted structural tensions among municipal fiscal sustainability, contractor transparency, and data consistency.
Using an evidence-based approach, a data preparation pipeline powered by AI algorithms was deployed to infer missing consumption and demand metrics with high accuracy. This intelligence was integrated into an interactive Power BI dashboard, enabling regulators to audit variances in real time and simulate distribution scenarios. The outcome was a strategic management platform built to maximize the social return on public transit expenditure.
