Our paper titled “Explorative Public Transport Flow Analysis from Uncertain Social Media Data” in the GeoCrowd 2014 workshop of the ACM SIGSPATIAL conference has been selected by the workshop organizers as its best paper.
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.
Please click on the image to open the interactive WebGL
Our WebGL visualization at http://koenigstuhl.geog.uni-heidelberg.de/opentrafficflow/ shows increasing counts of network matched social media posts clustering outside London and moving into the center between 6-12 am and 6-12 pm. This typical commuting behavior can be detected along the whole railway network while posts between 12-6pm are spatially concentrated along the inner transportation network of London.
Steiger, E. Ellersiek, T. Zipf, A. (2014): Explorative public transport flow analysis from uncertain social media data. Third ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information (GEOCROWD) 2014. In conjunction with ACM SIGSPATIAL 2014. Dallas, TX, USA. dx.doi.org/10.1145/2676440.2676444