PowerBI Dashboard -

Analyzing Public Transport Delay

In this project, I built an interactive Power BI dashboard to analyze public transportation delays across multiple cities. The dashboard helps answer key questions such as:
What is the overall delay level?
Which city has the highest delays?
How do delays change day-by-day?
What are the main causes of delays?

The report also includes filters so users can explore delays by -
City, Transport Type (Bus/Metro), and Peak Hour (Yes/No).

Let's have a step by step look at my analysis using Tableau -

◾ My Goal -

My goal is to analyze public transport delays using Power BI to identify key patterns, causes, and peak-time issues that can help improve service efficiency and reliability.

◾ Prepared and Cleaned the data -

Checked column types (Date, City, Delay minutes, Delay Reason, Transport Type, Peak Hour) and ensured values were consistent for accurate visuals.

◾ Created Core Measures (DAX / Aggregations)

In Tableau, I created clear visuals to communicate the relationships: Created measures like Peak vs Non-Peak hours and Average Delay (mins) and used aggregations to compare delays across cities, time, and categories.

◾ Built Dashboard Visuals + Slicers -

Designed a clean one-page report with KPI cards, trend analysis, comparison chart, and cause breakdown, plus interactive filters.

◾ Tested Interactivity -

Verified that slicers (City, Transport Type, Peak Hour) update all visuals correctly and provide user-driven insights.

VISUALIZATIONS -

KPI Card: Average Public Transport Delay

This KPI shows the overall delay performance across the dataset. The dashboard indicates an average delay of 10.52 minutes, which represents the baseline service reliability level.

Which city experiences higher transportation delays?

This chart compares the average delay by city to identify where delays are most severe. Results show:

Chicago: 11.50 mins (highest)
Boston: 10.56 mins
New York: 9.50 mins (lowest)

Line/Area Chart: “How average delays change over time
(day by day)

This visual tracks the average delay trend across days to reveal fluctuations and spike days. It helps identify instability in service and highlights periods where delays increase sharply;
(for example, the trend reaches 20 minutes at the end of the period shown).

Donut Chart: “Major causes of transport delays

This chart breaks down delays by reason (Weather, Traffic, Maintenance, Signal Issue). It helps stakeholders understand what to prioritize;
(for example: if Weather and Traffic dominate, solutions may focus on real-time routing, scheduling buffers, or emergency plans).

Slicers (Filters): City, Transport Type, Peak Hour

These slicers allow users to explore and compare delays by:

City (Boston/Chicago/New York)
Transport Type (Bus/Metro)
Peak Hour (Yes/No)
This interactivity makes the dashboard useful for different audiences and scenarios.

Peak vs Non-Peak Summary (Additional Page Insight)

This section compares delays during peak vs non-peak time. The report shows:

Peak Avg Delay: 16.76 mins
Non-Peak Avg Delay: 1.67 mins
Peak Delay Increase %: 9.1%

◾ Key Findings -

Public transport delays average ~10.52 minutes, showing moderate delay impact overall.
Chicago has the highest delay compared to Boston and New York, indicating a need for stronger operational improvements there.
Delays change day-by-day with noticeable spikes, which suggests delays are affected by variable conditions such as traffic, weather, or system issues.
The cause breakdown helps identify which factors should be addressed first (Weather/Traffic/Maintenance/Signal issues).
Peak hours create significantly higher delays than non-peak periods, meaning congestion and high demand strongly impact reliability.

◾ Skills Demonstrated -