Introduction:

As part of the Maven Rail Challenge, we were tasked with playing the role of a BI Developer for SwiftRail Transport, a fictional train service provider. The goal was to create an exploratory dashboard to analyze traveler behavior and operating performance. This article explores the data analysis performed on SwiftRail Transport’s operations, uncovering key insights and trends that drive the company’s fictional performance.

Business Problem:

SwiftRail Transport aims to optimize its fictional operations and improve passenger satisfaction by analyzing traveler behavior and operating performance. The key business problems addressed in this analysis include:

1. Identifying the most popular routes (to ensure adequate resources are allocated).
2. Determining peak travel times (to manage capacity and reduce congestion).
3. Analyzing revenue from different ticket types & classes (to optimize pricing strategies).
4. Diagnosing on-time performance and (understanding) contributing factors (to minimize delays and cancellations).

Data Analysis Highlights

1. Trip Volume and Revenue:

A total of 29,773 trips occurred during the period, generating total revenue of £703,219. Peak periods contributed significantly to this revenue, with the morning peak (6 AM to 8 AM) generating approximately £279k and the evening peak (4 PM to 6 PM) contributing around £155k.

These figures highlight the high demand and financial importance of travel during commuting hours.

2. Popular Routes (Question 1):

Manchester Piccadilly to Liverpool Lime Street, with a total of 4,338 trips, is the most popular route (out of 64 routes). However, despite its popularity, it does not generate the highest revenue. The London Kings Cross to York route generates the most revenue, totaling £179,498, followed distantly by other routes from London.

This suggests that the Manchester Piccadilly to Liverpool Lime Street route might have lower fare prices or fewer premium ticket purchases compared to the London Kings Cross to York route.

3. Peak Travel Times (Question 2):

There are two peak travel periods during the day.

1. Morning Peak (6 AM — 8 AM): This period sees a high volume of commuters traveling to work, with a total of 7,497 trips.
2. Evening Peak (4 PM — 6 PM): This period experiences a similar surge as commuters return home from work, totaling 7,708 trips.

In contrast, very few trips occur at night (between 7 PM and 5 AM) and during midday (between 9 AM and 3 PM)

4. Revenue from Ticket Types and Classes (Question 3):

Between the two Ticket Classes, Standard tickets generate significantly more revenue than First Class tickets. Among the ticket types, Advance tickets bring in the highest revenue.

Specifically, Advance (Standard Class) tickets generated the most revenue, totaling £229,373. This highlights the popularity and financial impact of Advance tickets within the Standard class, indicating a preference for cost-effective travel options among passengers.

5. Analyzing On-Time Performance (Question 4-a):

Approximately 27,000 trips, representing 92% of successful journeys (and 86% of all bookings), were on time. In contrast, only a small number of trips experienced disruptions, with 2,292 trips delayed and 1,880 trips cancelled.

This high on-time performance rate highlights the overall reliability of the railway service, despite the challenges faced.

6. Analyzing Delays and Cancellations (Question 4-b):

A total of 1,880 trips were cancelled, with ‘Signal Failure’ cited as the top reason (519 trips cancelled due to Signal Failure). ‘Weather Conditions’ was the leading cause of delays, affecting 927 out of 2,292 delayed trips and resulting in 677 (out of over 1,600) hours of delay.

Additionally, customer requests for refunds due to these issues amounted to about £38,700. This highlights the significant impact of service disruptions on both passengers and the railway’s financial performance.

Recommendations:

1. Allocate Resources to Popular Routes: Increase train frequency and capacity on the Manchester Piccadilly to Liverpool Lime Street route to accommodate the high passenger demand.
2. Optimize Peak Travel Times: Adjust schedules and deploy additional staff during both Morning and Evening) peak travel periods to manage capacity and improve passenger experience.
3. Revise Pricing Strategies: Review and adjust ticket pricing, especially for the London Kings Cross to York route, to balance demand and maximize revenue.
4. Improve On-Time Performance: Invest in infrastructure and technology to minimize signal failures and enhance communication systems for better handling of weather-related disruptions.
5. Enhance Customer Service: Implement proactive measures to address delays and cancellations, including punctual notifications and time-saving refund processes, to maintain customer satisfaction.

Conclusion:

The Maven Rail Challenge provided a valuable opportunity to analyze fictional data for a UK Rail company (which I named SwiftRail Transport). This analysis revealed important insights into popular routes, peak travel times, revenue patterns, and causes of delays. It highlighted the critical role of data in optimizing operations and enhancing customer satisfaction. By implementing data-driven recommendations, a real rail service could improve efficiency, reliability, and overall passenger experience.