The collaboration with a network operator gives the project access to four exceptionally detailed data sets:
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An aggregated data set on long-distance travel behaviour patterns allowing a precise estimate of trips over 50 kilometres for the first time;
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Anonymised 50,000 person-days permitting the development of new algorithms to identify daily behaviour patterns;
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Anonymised 10,000 person-weeks facilitating the estimation of detailed traffic behaviour models;
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Aggregated, average hourly demand by month from each Swiss commune to every other.
Work will comply with data-protection regulations and aims to improve and calibrate the Switzerland model, which we will construct using the individual-based software MATSim.
Many city road and rail trips face bottlenecks during peak periods, whereas for the remainder of the time infrastructure utilisation is low. Smartphone data provides a comprehensive picture of usage of a city. Drawing on anonymised data from network operator subscribers, this project aims to refine an individual-based model of Switzerland – and to develop an approach with dynamic transport prices that is capable of supporting Swiss transport policy.
By way of example, we will develop new modelling methods based on anonymised mobile phone data sets. The objective behind modelling these electronic traces is to enhance current individual-based simulations – and explore and optimise example scenarios for the possibilities, costs and limitations of a Swiss mobility pricing approach.
Transport models can be constructed faster and calibrated more reliably using large volumes of data. The models will serve to improve traffic system control and thus help to smooth infrastructure peak loads. The data-optimised MATSim model of Switzerland and the mobility pricing findings have the potential to make a key contribution to maximising transport system efficiency, which in turn will enhance the well-being of everyone.
Big data transport models: The example of road pricing
