Journal of Algorithms
On map-matching vehicle tracking data
VLDB '05 Proceedings of the 31st international conference on Very large data bases
VTrack: accurate, energy-aware road traffic delay estimation using mobile phones
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
Hidden Markov map matching through noise and sparseness
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Map-matching for low-sampling-rate GPS trajectories
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Routing-based map matching for extracting routes from GPS trajectories
Proceedings of the 2nd International Conference on Computing for Geospatial Research & Applications
Computing with Spatial Trajectories
Computing with Spatial Trajectories
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
| Hi-index | 0.00 |
The process of map matching takes a sequence of possibly noisy GPS coordinates from a vehicle trace and estimates the actual road positions---a crucial first step needed by many GPS applications. There has been a plethora of methods for map matching published, but most of them are evaluated on low-noise datasets obtained from a planned route. And comparisons with other methods are very limited. Based on our previous unifying framework used to catalog different mathematical formulas in many published methods, we evaluate representative algorithms using the low-noise dataset from the GIS Cup 2012 and a high-noise dataset collected from Shanghai downtown. Our experiments reveal that global max-weight and global geometrical map matching methods are the most accurate, but each has its weaknesses. We therefore propose a new map matching algorithm that integrates Fréchet distance with global weight optimization, which is more accurate across all sampling intervals.