Computational-geometric methods for polygonal approximations of a curve
Computer Vision, Graphics, and Image Processing
Fibonacci Heaps And Their Uses In Improved Network Optimization Algorithms
SFCS '84 Proceedings of the 25th Annual Symposium onFoundations of Computer Science, 1984
Understanding mobility based on GPS data
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Mining user similarity based on location history
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Trajectory simplification method for location-based social networking services
Proceedings of the 2009 International Workshop on Location Based Social Networks
Hidden Markov map matching through noise and sparseness
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Mining significant semantic locations from GPS data
Proceedings of the VLDB Endowment
Recommending friends and locations based on individual location history
ACM Transactions on the Web (TWEB)
SQUISH: an online approach for GPS trajectory compression
Proceedings of the 2nd International Conference on Computing for Geospatial Research & Applications
Unveiling the complexity of human mobility by querying and mining massive trajectory data
The VLDB Journal — The International Journal on Very Large Data Bases
Computing with Spatial Trajectories
Computing with Spatial Trajectories
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
IEEE Transactions on Information Theory
TrajMetrix: a trajectory compression benchmarking framework
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Pathlet learning for compressing and planning trajectories
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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Many devices nowadays record traveling routes, of users, as sequences of GPS locations. With the growing popularity of smartphones, millions of such routes are generated each day, and many routes have to be stored locally on the device or transmitted to a remote database. It is, thus, essential to encode the sequences, to decrease the volume of the stored or transmitted data. In this paper we study the problem of coding routes over a vectorial road network (map), where GPS locations can be associated with vertices or with road segments. We consider a three-step process of dilution, map-matching and coding. We present two methods to code routes. The first method represents the given route as a sequence of greedy paths. We provide two algorithms to generate a greedy-path code for a sequence of n vertices on the map. The first algorithm has O(n) time complexity, and the second one has O(n2) time complexity, but it is optimal, meaning that it generates the shortest possible greedy-path code. Decoding a greedy-path code can be done in O(n) time. The second method codes a route as a sequence of shortest paths. We provide a simple algorithm to generate a shortest-path code in O(kn2 logn) time, where k is the length of the produced code, and we prove that this code is optimal. Decoding a shortest-path code also requires O(kn2 logn) time. Our experimental evaluation shows that shortest-path codes are more compact than greedy-path codes, justifying the larger time complexity.