Inference of Transportation Networks from Sparse Tracking Data

Sophie Karagiorgou

25th March 2015 (Wednesday), 14:00 in ATT 002


The commoditization of tracking technology, e.g., smart-phone applications involving check-ins, real-time navigation applications, fleet management, etc., provides us with a wealth of tracking data that, when utilized properly, will allow us to derive road and transportation networks. The research challenges in the form of map inference methods have been addressed to a limited extent in literature. Existing methods are characterized by limited geographical scope, small-scale tracking datasets, and unconvincing map construction results. Thus, sophisticated map inference algorithms are needed to improve over the current shortcomings and provide methods that can also be used in a practical setting. The present approach contributes to this knowledge by proposing automatic transportation map inference algorithms for the simpler case of road networks, and the more complex case of semantically more expressive multimodal networks-of-interest. Towards the goal of automatic road network inference, we propose two different techniques. The merit of these techniques is the automatic inference of navigable road networks of high spatial accuracy with respect to their geometry, enriched with additional attributes such as permitted maneuvers and road categories. Besides GPS tracking data and road networks, we also present a novel technique for dealing with user generated geospatial tracking data derived from social media applications. The proposed method allows us to discover transportation hubs and critical transportation infrastructure from geocoded tweets. To further motivate and facilitate researchers and practitioners working in this area, we have created which is an online repository containing source code of the state-of-the-art algorithms, as well as datasets for testing and evaluation. Finally, to investigate and demonstrate the applicability of our map inference algorithms in additional application domains, the map-construction approach has been applied to the visualization of eye tracking data.

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