Mobile Network Data –Station Access Patterns

Mobile Network Data –Station Access Patterns

Contract Camden Station, London – Study by Tracsis Partner, Citi Logik
Service Origin and destination Surveys
Scale Over 250,000 weekday walking journeys between Camden station and the surrounding area were identified and analysed
Project Location Camden Station and surrounding area (walking distance)

Transport for London (TfL) and London Underground (LU) were sourcing alternative ways to legacy surveys to collect Origin-Destination data for station users in the Capital. The results would be used to improve station designs by providing better accessibility and entry/exits options through ticket hall redevelopment. Transport for London (TfL) and London Underground (LU) asked Citi Logik if there were other ways to glean this information on which they could base station configuration decisions.

TfL wanted to use the analysis and visualisation of Mobile Network Data (MND) an offering which is unique to Citi Logik in the UK market to track the origin and destination of passengers using Camden station. Through this method, it becomes possible to track users of any station to and from their final destinations. TfL commissioned Tracsis partner, Citi Logik to undertake such an OD survey for Camden station to assess whether it was sufficiently robust to make decisions about station improvements.

In partnership with Tracsis, Citi Logik has unique access to anonymised data from the 3/4G network which allows it to create comprehensive and high value understandings of the urban environment.  This capability is genuinely innovative, capturing anonymised and aggregated information from millions of ever-moving SIM cards (mobile phones), facilitating analysis across the city, every minute of every day, for every major road, rail route and pedestrian walkway. Citi Logik complies to the highest global standards of data protection and individual privacy.

The scope of the project was to provide TfL LU with pedestrian passenger Origin and Destination (OD) tracking data at Camden Station, using cellular mobile data from the Vodafone network, within the area defined by TfL LU.  The data capture period covered a 4 week period. The data was processed against Citi Logik optimised locations (based on aggregated Vodafone ‘mast’ locations). In addition, video visualisation of the point data of pedestrian movement was included for a 24hr period by 15 minute  intervals to illustrate how passenger OD to and from the Camden station catchment area grows and intensifies at certain locations within the boundary.

Key data processing stages of the project were:

  • Step 1 – Data Collection
  • Step 2 – Identification of Unique Anonymised Mobile Users
  • Step 3 – Breakdown of Anonymised Journeys (All Modes)
  • Step 4 – Walking Mode Anonymised Journeys
  • Step 5 – Filtered Allocated Walking Journeys
  • Step 6 – Expansion to Gateline Data

Over the course of the survey period, over 250,000 weekday walking journeys between Camden station and the surrounding area were identified and analysed. Comparison with gate line data suggests that this corresponds to about 20% of all the trips using Camden station over the 4 weeks of the study. Outputs including pie chart distributions and heat maps showing the main catchment areas for Camden LUL station users were produced.

The results of the survey using MND compared favourably to findings from traditional methods. As part of the final product videos files were produced which covered a 24hr period and provided a visualisation of the point data of pedestrian movement by 15 minute intervals to show how certain locations within the Camden catchment grow and intensify over the course of a typical weekday.

Heat maps are just one way in which we make Mobile Network Data  analysis  ‘come alive’


The ability to accurately discover where customers start and end their overall journeys is incredibly valuable for owners and managers of static locations. The output variables of projects like this can guide them on practical steps that they can take to attract people to stay or flow more quickly depending on desired objectives. The advantages of a Mobile Network Data analysis approach to this assignment proved to be:

  • Complying with global privacy standards
  • Up-to-date timely data;
  • Non-intrusive on Gateline operations;
  • The technology compliments Oyster / Plinth (LU Gateline count) data;
  • Low cost and high value datasets; and
  • Innovative approach and an opportunity for TfL LU to use a future technology

The results confirmed that the MND approach to OD surveys works and provides sensible outputs for use to TfL LU to help them gain insights about who uses Camden station and what their final origin and destinations are as pedestrians.  Following the success of this assignment in identifying and analysing OD passenger movements, the Citi Logik and Tracsis team are now working to investigate the best ways to utilise the methodologies associated with MND analytics to gain insights about who uses specific locations and what their final origin and destinations are as pedestrians.