SRH University

Local Commute SRH University Berlin

How do students and employees navigate Berlin's transport network to reach the university - and how can these commuting patterns be improved for everyone? The Smart Access Flows SRH Berlin project explores these questions using surveys, mobility data, and advanced analytical methods. By combining behavioural insights with spatial and temporal analyses, the project builds a detailed understanding of how the campus connects to the wider urban mobility system.

A paved path along a canal, lined with trees and outdoor seating, with modern buildings in the background.
A yellow subway train travels along the tracks, with historic buildings featuring towers and a clear sky in the background.

Understanding access and mobility patterns around Sonnenallee

Special attention is given to multimodal journeys, last-mile connectivity, and the role of sustainable transport options such as cycling and public transport. Building on this, the project identifies concrete improvement opportunities through the university as employer and education provider, including targeted mobility offers such as discounted or integrated public transport tickets, company bike and campus bike programs, as well as car sharing and ride pooling cooperations. Further options would be attractive remote work and hybrid teaching arrangements, as well as enhanced on-campus facilities such as secure bike parking, showers, and changing rooms. 

Together with students and employees, commuting patterns to and from campus are analysed through surveys, voluntary tracking tools, and anonymised mobility data. The focus lies on how people travel across Berlin using different modes of transport and how they access the campus. 

This includes journeys via Berlin U-Bahn, Berlin S-Bahn, trams, buses, walking and cycling routes, and private mobility options. The data reveals key insights into peak arrival and departure times, preferred transport modes, major interchange points, and differences between student and employee commuting behaviour. 

These findings are particularly relevant in light of Berlin's growing mobility demands, climate targets, and pressure on public transport infrastructure. On this basis, the university can develop tailored measures: 

  1. Flexible teaching and working schedules to spread demand over time
  2. Campus shuttle options to major hubs
  3. Information and advisory services on sustainable mobility
  4. Mobility budgets or subsidies for climate-friendly modes 

Understanding access flows thus enables more efficient, sustainable, and user-friendly integration of the campus within the city. 

A yellow streetcar glides along a wall while two people stroll on the sidewalk. Bright clouds fill the sky.

From data to practical recommendations

The results are processed to directly support university stakeholders and mobility planners in improving campus accessibility. All data used in this analysis are fully anonymised and contain no personal identifiers. Individual students or employees cannot be traced or identified from the data. The analysis focuses exclusively on general traffic flows and aggregated mobility patterns of these groups, not on individual behaviour. 

This includes clearly defined commuter profiles, recommendations for staggered schedules to reduce peak congestion, insights into public transport usage and bottlenecks, and strategies to encourage sustainable commuting -such as improved bike infrastructure or better alignment with public transport timetables. 

In addition, internal university measures are proposed: 

  1. Mobility guidelines for business trips
  2. Incentives for climate-friendly commuting decisions
  3. Digital teaching and meeting formats
  4. Campus-based services such as sharing stations and bicycle repair services 

The outcome is a practical, data-driven foundation for reducing congestion, supporting Berlin's sustainability goals, and enhancing the daily commute for students and employees through improved offers provided by the university and employer.

Logo of Motiontag with the slogan 'Discover how people move.

Technology & data protection

For the high-resolution, GDPR-compliant collection of mobility patterns, the project uses the technology of its partner motiontag. The software automatically detects modes of transport and reconstructs mobility trajectories without requiring time-consuming manual input. This enables robust, evidence-based insights into typical routes, behavioural changes and the effects of digital visitor management tools. Detailed information on app usage and data protection is available on this page.

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