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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The paper describes a map generation system which relies on random individual contributions of GPS traced movements. In a typical use case, mobile phone users would join a specific community to contribute their movements along streets, roads or pathways in form of so called traces. Neither contributing subscribers nor the map generation need to have an a priori knowledge of the charted area. The approach presented here comprises the trace recording, the upload process to a common server and the processing algorithm for map generation. The problem of noisy data, resulting from GPS inaccuracies and random movements is addressed and countermeasures are proposed. One major perspective of this paper is the decomposition of a map into reasonable segments. In this context, the filter mechanism applied to the raw data is a key component for the deduction of precise street maps. Furthermore, the proof of concept is given and a guideline for a lower limit of active subscribers to achieve an operational system is derived.