EasyTracker: automatic transit tracking, mapping, and arrival time prediction using smartphones
Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
Mining large-scale, sparse GPS traces for map inference: comparison of approaches
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Map inference in the face of noise and disparity
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
CrowdAtlas: self-updating maps for cloud and personal use
Proceeding of the 11th annual international conference on Mobile systems, applications, and services
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Over the last few years, electronic vehicle guidance systems have become increasingly more popular. However, despite their ubiquity, performance will always be subject to availability of detailed digital road maps. Most current digital maps are still inadequate for advanced applications in unstructured environments. Lack of up-to-date information and insufficient refinement of the road geometry are among the most important shortcomings. The massive use of inexpensive Global Positioning System (GPS) receivers, combined with the rapidly increasing availability of wireless communication infrastructure, suggests that large amounts of data combining both modalities will be available in the near future. The approach presented here draws on machine-learning techniques and processes logs of position traces to consistently build a detailed and fine-grained representation of the road network by extracting the principal paths followed by the vehicles. Although this work addresses the road-building problem in dynamic environments such as open-pit mines, it is also applicable to urban environments. New contributions include a fully unsupervised segmentation method for sampling roads and inferring the network topology, which is a general technique for extracting detailed information about road splits, merges, and intersections, as well as a robust algorithm that articulates these two. Experimental results with data from large mining operations are presented to validate the new algorithm.