Authors: Sophia Gross-Fengels*, RWTH Aachen University
Topics: Transportation Geography, Quantitative Methods, Economic Geography
Keywords: Mobile Network Data, Smart Mobility, Sustainable Mobility
Session Type: Paper
Presentation File: No File Uploaded
Rising overall transport volumes and increasingly individualistic travel demands pose ever greater challenges to the design of efficient and sustainable transportation systems. So far, ample user surveys and rigid locally fixed counting machines have provided cost-intensive data input for public planning decisions, even though they often only provide approximate, time-shifted values. Hence, the vast quantities of user-generated Mobile Network Data (MND), set up promising insights through individual tracking.This surely raises questions regarding personal data security, but also offers interesting opportunities to critically evaluate spatial differ-ences and possible inequalities of everyday mobility patterns.Following an interdisciplinary approach, we analysed a MND-dataset provided by an international telephone company for the comparison of an urban (Berlin, Germany) versus rural (Heinsberg, Germany) case study area. Quantitative analysis was performed by application libraries and modules (Python, MS Excel), moreover data visualisation with ArcGIS allowed space-specific evaluations.These results were then aligned with governmental planning data, detecting mobility demands, followed by planning recommendations. Our results reveal not only interesting urban versus rural mobility patterns, but also show opportunities and limitations for insights on exclusion and inequalities MND have to offer. In Berlin the significance surplus of the city centre becomes clear, whereas in the rather decentralised district of Heinsberg traffic is widely spread, even though mide-size centres exist: the high-resolution MND provides valuable insights especially for such heterogeneous spaces. While significant findings can be gained up to district level, limitations occur when transferring individual behaviour to the overall transport network and for insights on detailed spatial inequality patterns.
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