Generating synthetic mobile phone datasets using MATSim

Abstract

Passively collected datasets of mobile phone traces are increasingly used for the generation of transportation models. Datasets can contain more than 2000 location events per person per day and can observe hundreds of thousands of participants with no response burden. Hence, such datasets are very attractive for transport modelling, particularly on a regional level. However, privacy regulations make accessing, working with, and sharing such data challenging. We propose an approach for the generation of open, synthetic mobile phone traces, based on a small sample of network traces, information on the location of the network antennas, and activity patterns from a MATSim scenario. Such datasets will allow for better collaboration between researchers on the development of new algorithms for extracting travel plans and other indicators. Previous approaches only generated synthetic traces for CDR (Call detail record) data, which contains many less data points than traces from 3G and 4G networks. The method accommodates different network types (GSM, 3G, 4G etc), and the introduction of important data artefacts such as pinging and loss of reception. Using the proposed method, a the first steps towards a synthetic network trace dataset for Switzerland calibrated from Austrian network traces is presented.