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xMND project: How mobile phone data could revolutionise transport modelling

Although man has already flown to the moon, he still has trouble working out and predicting the number of passengers on the 963 bus that runs between Bad Salzuflen and Lemgo. This is largely due to the fact that up to now recording transport flows, especially passenger transport, has been very selective in terms of time and place. Transport surveys or count points are examples of this kind of recording with their time / location restriction.

The widespread use of mobile devices is opening up entirely new possibilities here. After all, every owner of a smartphone carries a digital sensor that records movement data every second. In practice, this can be seen in very detailed tracking data, such as the data collected by MotionTag using special apps, or Mobile Network Data (MND), which is provided in Germany, for instance, by Telefónica NEXT.

In the xMND project, which is funded by the mFUND research initiative of the Federal Ministry of Transport and Digital Infrastructure (BMVI), both data sets, i.e. MND and precise tracking data sets, are now being compiled. The consortium for the research project, which is planned until mid-2020, is made up of Telefónica Germany NEXT GmbH, civity Management Consultants GmbH & Co. KG (civity), Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS) and MotionTag GmbH (MT). At the start of 2019, the first data collections will be carried out in Leipzig and the district of Lippe. The aim is to boost the validity of MND for the various means of transport by using a ground-truth analysis, i.e. by comparing the special on-site survey with the comprehensive databases (Fraunhofer IAIS, MotionTag, Telefónica NEXT).

civity’s task is to collect the requirements of the partners on the ground and to transfer these requirements into an overall concept. The improved data basis will then be exploited in two application-oriented developments: transport modelling and revenue sharing in public transport. The prototypical developments will also be carried out by civity. It is expected that this will generate very specific added value for transport planning and public transport operations.

Transport modelling has already experienced a boost in recent decades thanks to increased data availability and improved computing power. Another driving force is the fact that new mobility providers can start from scratch with a white sheet of paper and methodically plan their services. The layout of operating areas in free floating car sharing, for instance, is already based on MND. With the increase in MND, the question arises as to whether traditional approaches to collecting transport-related data could become obsolete in the future, at least when it comes to presenting the status quo of transport. Transport companies and associations are slowly catching up – although some public transport companies have of course already taken on a pioneering role in the past.

The interim project results will also be used to develop a revenue sharing system. In Germany, the sharing of passenger revenue between several transport companies participating in a common fare system constitutes a sort of “Holy Grail”, since any change or adjustment of agreements concluded in the past has a very concrete impact on operator revenue. On the other hand, a more dynamic distribution of revenues benefits competition and ultimately the end customer. There is no question, however, that the principles of public service must be taken into account here. civity would like to address this interest with a new approach.

The revenue sharing concept developed by civity will be presented in mid-2019. Initial findings from the pilot regions are expected by the end of 2019.