On map-matching vehicle tracking data
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Maximum entropy inverse reinforcement learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Tracking many objects with many sensors
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Hidden Markov map matching through noise and sparseness
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
T-drive: driving directions based on taxi trajectories
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Driving with knowledge from the physical world
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Path shapes: an alternative method for map matching and fully autonomous self-localization
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Learning driving behavior by timed syntactic pattern recognition
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Behaviour recognition in smart homes
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
On vehicle tracking data-based road network generation
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Detecting movement patterns using Brownian bridges
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
An algorithm for map matching given incomplete road data
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Matching GPS traces to (possibly) incomplete map data: bridging map building and map matching
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Effective map-matching on the most simplified road network
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Fast Viterbi map matching with tunable weight functions
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
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We study map-matching, the problem of estimating the route that is traveled by a vehicle, where the points observed with the Global Positioning System are available. A state-of-the-art approach for this problem is a Hidden Markov Model (HMM). We propose a particular transition probability between latent road segments by the use of the number of turns in addition to the travel distance between the latent road segments. We use inverse reinforcement learning to estimate the importance of the number of turns relative to the travel distance. This estimated importance is incorporated in the transition probability of the HMM. We show, through numerical experiments, that the error of map-matching can be reduced substantially with the proposed transition probability.