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
CrowdAtlas: self-updating maps for cloud and personal use
Proceeding of the 11th annual international conference on Mobile systems, applications, and services
Map matching: comparison of approaches using sparse and noisy data
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Point-polygon topological relationship query using hierarchical indices
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Map matching with inverse reinforcement learning
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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This paper describes a map matching program submitted to the ACM SIGSPATIAL Cup 2012. We first summarize existing map matching algorithms into three categories, and compare their performance thoroughly. In general, global max-weight methods using the Viterbi dynamic programming algorithm are the most accurate but the accuracy varies at different sampling intervals using different weight functions. Our submission selects a hybrid that improves upon the best two weight functions such that its accuracy is better than both and the performance is robust against varying sampling rates. In addition, we employ many optimization techniques to reduce the overall latency, as the scoring heavily emphasizes on speed. Using the training dataset with manually corrected ground truth, our Java-based program matched all 14,436 samples in 5 seconds on a dual-core 3.3 GHz iCore 3 processor, and achieved 98.9% accuracy.