Abstract
Directions and paths, as commonly provided by navigation systems, are usually derived considering absolute metrics, e.g., finding the shortest or the fastest path within an underlying road network. With the aid of Volunteered Geographic Information (VGI), i.e., geo-spatial information contained in user generated content, we aim at obtaining paths that do not only minimize distance but also lead through more popular areas. Based on the importance of landmarks in Geographic Information Science and in human cognition, we extract a certain kind of VGI, namely spatial relations that define closeness (nearby, next to) between pairs of points of interest (POIs), and quantify them following a probabilistic framework. Subsequently, using Bayesian inference we obtain a crowd-based closeness confidence score between pairs of POIs. We apply this measure to the corresponding road network based on an altered cost function which does not exclusively rely on distance but also takes crowdsourced geo-spatial information into account. Finally, we propose two routing algorithms on the enriched road network. To evaluate our approach, we use Flickr photo data as a ground truth for popularity. Our experimental results – based on real world datasets – show that the paths computed w.r.t. our alternative cost function yield competitive solutions in terms of path length while also providing more “popular” paths, making routing easier and more informative for the user.
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Acknowledgements
The research leading to these results has received funding from the EU FP7 project GEOSTREAM (grant No. FP7-SME-2012-315631) as well as the Shared-E-Fleet project by the German Federal Ministry of Economics and Technology (grant No. 01ME12107), the Deutsche Forschungsgemeinschaft (DFG) under grant number RE 266/5-1 and from the DAAD supported by the BMBF under grant number 57052426. Mario A. Nascimento has been partially supported by NSERC Canada. Dieter Pfoser has been partially supported by NGA NURI (grant No. HM02101410004).
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Skoumas, G. et al. (2015). Knowledge-Enriched Route Computation. In: Claramunt, C., et al. Advances in Spatial and Temporal Databases. SSTD 2015. Lecture Notes in Computer Science(), vol 9239. Springer, Cham. https://doi.org/10.1007/978-3-319-22363-6_9
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DOI: https://doi.org/10.1007/978-3-319-22363-6_9
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