Generating Network-Based Moving Objects
SSDBM '00 Proceedings of the 12th International Conference on Scientific and Statistical Database Management
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hidden Markov map matching through noise and sparseness
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Where traffic meets DNA: mobility mining using biological sequence analysis revisited
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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Map matching is a fundamental operation in many applications such as traffic analysis and location-aware services, the killer apps for ubiquitous computing. In past, several map matching approaches have been proposed. Roughly, they can be categorized into four groups: geometric, topological, probabilistic, and other advanced techniques. Surprisingly, kernel methods have not received attention yet although they are very popular in the machine learning community due to their solid mathematical foundation, tendency toward easy geometric interpretation, and strong empirical performance in a wide variety of domains. In this paper, we show how to employ kernels for map matching. Specifically, ignoring map constraints, we first maximize the consistency between the similarity measures captured by the kernel matrices of the trajectory and relevant part of the street map. The resulting relaxed assignment is then "rounded" into a hard assignment fulfilling the map constraints. On synthetic and real-world trajectories, we show that kernels methods can be used for map matching and perform well compared to probabilistic methods such as HMMs.