This project analyses Dublin's public bike-sharing dataset to understand usage patterns, station demand, and operational performance.
Using Power BI, the dataset was transformed into an interactive dashboard that visualises trip volumes, peak demand periods, and station utilisation across the city.
The analysis demonstrates how mobility data can be used to support data-driven decisions in urban transport planning and resource allocation.
- Power BI
- Data Visualisation
- Data Modelling
- KPI Dashboarding
- Data Cleaning
Power BI dashboard visualising bike trip activity and station performance.
Identifies peak usage periods throughout the day and across locations.
Highlights high-traffic stations and underutilised locations.
Provides insights that could support capacity planning and bike redistribution strategies.
Cleaned and structured the dataset to ensure consistent formatting of trip data and station information.
Loaded the dataset into Power BI and created calculated metrics for analysis.
Designed visual components including usage trends, demand patterns, and station activity.
Analysed dashboard outputs to identify peak demand periods and station performance differences.
- Analysed 180K+ bike trips
- Identified 12 peak demand periods
- Revealed 5 underutilised stations
These insights demonstrate how data analytics can support urban mobility planning and operational optimisation.
Puneeth Rao
Business & Information Systems Student
Data Analytics & Business Intelligence