Rail travel experiences are significantly influenced by the accuracy and timeliness of information provided to passengers. Darwin, the UK’s rail information engine, offers a wealth of real-time data but optimizing its potential to enhance passenger journeys presented a complex challenge. This case study explores how we at BayRock Labs employed a data-driven approach to assess the accuracy of Darwin's predictions and uncover opportunities to improve the overall rail travel experience through data-driven insights and visualizations.
End-to-End Data Pipeline
Collected real-time (STOMP) and historical (FTP) data, stored in a PostgreSQL database for centralized access.
Comprehensive Analysis
Leveraged Darwin platform and custom tools to analyze train status and schedule events.
Insightful Visualization
Created data visualizations to uncover key patterns and trends for informed decision-making.
to collect, store, and manage real-time and historical rail data for centralized analysis.
using Darwin’s platform and custom tools to evaluate prediction accuracy and operational patterns.
to uncover trends, enabling data-driven decisions to enhance passenger experience and optimize rail operations.