BIXI Operations Dashboard

Bike-sharing operations generate large volumes of station-level data that change throughout the day. Raw data alone does not help operational teams quickly understand where bicycles are available, which stations are under pressure or when demand patterns change.

The challenge was to transform multiple operational indicators into a clear dashboard capable of supporting geographic, temporal and station-level analysis.

The solution combines station information with frequently updated station status data through a structured analytical model.

Power Query was used to prepare and transform the source data, while DAX measures were developed to calculate current availability, capacity, full stations, empty stations and operational usage indicators.

The dashboard was designed around the operational questions users need to answer quickly: where availability problems are occurring, when bike availability changes and which stations require attention.

Azure Maps provides the geographic perspective, KPI cards summarize network conditions and supporting charts expose hourly patterns and low-availability stations.

The dashboard helps identify stations with low bicycle availability, stations approaching capacity and periods where operational intervention may be required.

By consolidating these signals into one interface, the report supports faster interpretation and creates a stronger foundation for bike redistribution and service planning.