C4.5: programs for machine learning
C4.5: programs for machine learning
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Mapping Hacks
From GPS traces to a routable road map
Proceedings of the 17th ACM SIGSPATIAL 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
Map matching: comparison of approaches using sparse and noisy data
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Hi-index | 0.00 |
This paper introduces a novel offline map matching approach. We develop a routing-based map matching approach for standardizing identified routes in a collected set of GPS trajectories. Our approach first identifies key waypoints in a user's GPS trajectory using a modified Peucker curve reduction algorithm. Subsequently, it sends the key waypoints to a black-box driving directions service which returns a route utilizing each of the key waypoints. The returned route is a standardized representation of the original GPS trajectory constructed using the minimum necessary set of points. A filter-and-refine approach is used to identify the incorrect portion of the returned route and a refine step is carried out by eliminating the waypoints what leads to the incorrect matching. Experiments results showed that the proposed approach works well for a data-set of 10 volunteers each collecting data an average of 34.3 days.