Anonymized signalling data from the mobile communication network provides a comprehensive sample since most people use a cell phone, send text messages, or use the mobile Internet on a daily basis. Network activities are analysed statistically, enabling us to create anonymous statements about traffic and movement flows on a daily basis – statements that have previously been virtually impossible to make. The analysis of these movement flows can be further enriched with socio-demographic traits, allowing for statements about age groups, gender distribution and the state or city of residence of transport users.
The data used is, of course, anonymous. The processes used in working through the data were developed in close consultation with Germany’s Federal Commissioner for Data Protection and Freedom of Information and are checked and certified by external data protection test bodies.
Projections are provided via a Web frontend or an API.
Our evaluations cover the following:
Traffic censuses at “virtual counting stations” (private traffic, number of pedestrians)
The speed of traffic flow
Source-target analyses of traffic flows
Differentiation between modes of transport
An aggregated socio-demographic split of transport users (age group, sex, district/city or state)
In the autumn of 2014, we jointly carried out a pilot project with the public transportation operator of a large German city. Based on anonymous signalling data from the mobile communication network, new findings were delivered to help optimize traffic. A key component was the census figures, for which previously no, or only very little data, was available. The findings included the following:
Which traffic flows currently exist between specific city districts?
How intensively are individual regional transportation routes used?
To what extent does the daily population vary in relevant city districts from which commuters travel downtown to work?
The results confirmed assumptions and provided new findings. The traffic flow figures between individual city districts were especially telling. One of the objectives is to identify districts in which private transportation is on the rise due, for example, to new housing projects. These districts are where new bus routes could serve the increase in traffic. That would make public transportation more attractive. Each motorist who switches to public transportation helps to reduce exhaust emissions, noise and congestion in the city.
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