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Peak Spotting

Managing passenger loads in the German rail network

More and more people travel by train in Germany. How can we combine machine learning and visual analytics methods to help manage the passenger loads?

Peak Spotting provides yield and capacity managers with rich visual tools to identify potential bottlenecks early on and react through price management, communications or logistic solutions.

Client: Deutsche Bahn AG
Produced in cooperation with Studio NAND

Project director: Christian Au

Creative direction, data visualization: Moritz Stefaner

Technical lead: Stephan Thiel

Design: Christian Laesser

Development: Gabriel Credico, Lennart Hildebrandt

Analytics: Kevin Wang


Banner strip of Peak Spotting views: daily load histograms, an animated Germany rail map and a train timetable

Note: All images and screencasts on this page use realistic, but scrambled data.

Based on passenger load predictions from neural nets and random forest models, the web application integrates millions of datapoints over 100 days into the future, allowing to inspect the data on custom developed visual tools such as animated maps, stacked histograms, path-time-diagrams and powerful lists with miniature visualizations.

Special emphasis was put on providing actionable information and collaborative features, so that insights can be transformed immediately into improvements in planning and management.

Information architecture

The application is structured as a left-to-right journey from overview (hundred days with hundreds of trains each) to detail (a single leg on a single train). Two adjacent panels fit exactly on a full HD display, so it can be used well on one or two monitors.

Information architecture: four panels from Calendar overview to Day view, Train collection and Train details

UI details

Features like quick-search, powerful grouping, sorting and filtering options as well as the integration of task workflows transform the application from a mere data “observatory” to a tool that actually plays a practical role in everyday workflows.

Close-up of the UI: quick-search for trains plus filters for route, tasks, status, forecast and booking levels

Visual components

Based on a coherent visual language which maps important dimensions like actual bookings or prognosis values to meaningful visual dimensions, we developed a variety of data perspectives to allow planers to spot and isolate critical bottlenecks and tricky traffic situations.

Calendar of daily passenger-load histograms, highlighting 27.10.17 as a critical peak-travel day List of 701 trains with miniature timeline bar charts of booking levels, red marking overbooked segments Small-multiple maps of Germany by hour from 7:00 to 22:00 showing rail flows, red highlighting overloaded routes Grid of path-time diagrams per corridor, each train drawn as a line colored red to yellow by passenger load

Screencast

The application is not publicly available, but this screencast provides an impression of typical user interface actions and flows:

Impact

The application was a huge success and spawned a whole ecosystem of related services and apps. It has been in heavy use for over 4 years now at Deutsche Bahn. In 2021, it was recognized as one of the trail-blazing tools for the Deutsche Bahn as part of its “Digital DNA”.

Filtered to Hannover corridors: calendar overview beside hourly maps of train flows radiating from the hub

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