Explorative public transport flow analysis from uncertain social media data

Abstract

In this paper, we propose a framework to detect human mobility transportation hubs and infer public transport flows from unstructured georeferenced social media data using semantic topic modeling and spatial clustering techniques. An infrastructure for receiving and storing large sets of social media data has been developed together with an ad hoc processing framework in order to consider the high uncertainty of our retrieved data. Given the detected and extracted social media signals indicating human mobility, we compared the results with the public transport network from OpenStreetMap and classified observed mobility patterns for an exemplary case study. To analyze collected datasets a web based visualization tool has been setup.

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