UK National Rail [Honorable mention]

Task: For the Maven Rail Challenge, you'll play the role of a BI Developer for National Rail, a company that provides business services to passenger train operators in England, Scotland, and Wales.

You've been asked by your manager to create an exploratory dashboard that helps them:

• Identify the most popular routes
• Determine peak travel times
• Analyze revenue from different ticket types & classes
• Diagnose on-time performance and contributing factors

The dashboard consists of 4 parts:

1. Routes
2. Rush hours
3. Revenue
4. Delays

I. Routes

The most popular railway routes are presented in terms of the number of passengers and the number of railway connections. The second chart shows the number of passengers depending on the distance traveled.

An additional tab contains detailed information about all railway connections.

Data can be filtered depending on: month, day of the week and time (during peak or off-peak hours).

Special thanks to Chat GPT for calculating the distance between stations ;)

II. Peaks

The tab contains information about peak hours and how they are divided into individual days of the week and departure stations.

An additional tab contains information about peak hours at individual departure stations.

Data can be filtered depending on: month, day of the week, time (during peak or off-peak hours), ticket type and ticket type.

III. Revenue

The first chart contains information about revenues and income depending on the month and day of the week. The following charts show the same information, but depending on the Railcard, class and ticket type.

The donut chart shows refund information depending on whether the train was canceled or delayed. Additionally, the tooltip shows the distribution of refunds depending on the reason for the delay/cancellation and the delay time.

Data can be filtered depending on: month, day of the week, time (during peak or off-peak hours), ticket type and ticket type.

IV. Delays

The tab contains information about the time and reason for delays.

Data can be filtered depending on: month, day of the week and time (during peak or off-peak hours).

Sources:

The map on the first page comes from: www.pythonmaps.com