BlueCab Services offers cab rides from five airport pickup points across India. To enhance customer experience and operational efficiency, the company sought insights into how booking confirmations perform during peak and non-peak hours and the impact of outliers on booking outcome times. This project analyzes ride data and presents interactive visualizations in Power BI.
CabRidersTable.csv– Contains ride request details like timestamps, booking status, and outcome time
- What is the average booking efficiency in each pickup area during peak vs. non-peak hours?
- What are the average cancellation time (ACT), average booking time (ABT), and average booking outcome time (ABOT)?
- What percentage of rides are confirmed vs. unconfirmed?
- How do outliers (Z-Score > 2) impact booking outcome time?
- Imported CSV files into Power BI.
- Joined tables using Origin Area code.
- Classified rides based on Peak Time:
- Morning Peak: 5:00 AM – 9:00 AM
- Evening Peak: 6:00 PM – 11:00 PM
- Others: Non-Peak
Created a dashboard showing metrics for booking performance:
- Total Rides
- Confirmed Rides and Confirmed Ride %
- Unconfirmed Rides and Unconfirmed Ride %
- ACT (Average Cancellation Time)
- ABT (Average Booking Time)
- ABOT (Average Booking Outcome Time)
Filters:
- Time Category (Peak / Non-Peak)
- Calculated Z-Score for Booking Outcome Time
- Binned Z-Scores into 0.20 intervals
- Labeled Z-Scores:
> 2→ Outlier≤ 2→ Normal
- Created visual breakdown of ride distribution across bins
Built an additional dashboard to:
- Filter out outliers
- Compare metrics only for “Normal” data
- Help visualize how outliers skew booking trends
- Filter by Area shows that some zones underperform in confirmation rate during evening peak.
- Average Booking Outcome Time tends to increase during non-peak hours, possibly due to lower availability or system delays.
- Around 5–6% of rides are categorized as outliers, which may distort overall service KPIs.
- Reducing these outliers could enhance service consistency and reliability.
- Power BI
- DAX for calculated columns and measures – For ABT, ACT, ABOT, and Z-Score binning
- Power Query for data cleaning and transformation
- CSV files
The Power BI dashboard empowers BlueCab to:
- Make data-driven decisions during high-demand hours
- Identify and manage outliers that may skew performance metrics
- Prioritize efficiency improvements in critical pickup areas
This project sets the foundation for enhanced ride booking reliability, especially during peak periods.