Mining GPS Traces for Map Refinement
Data Mining and Knowledge Discovery
VTrack: accurate, energy-aware road traffic delay estimation using mobile phones
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
From GPS traces to a routable road map
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
IEEE Transactions on Information Theory
Tag configuration matcher for geo-tagging
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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Analysis of geographic data often requires matching GPS traces to road segments. Unfortunately, map data is often incomplete, resulting in failed or incorrect matches. In this paper, we extend an HMM map-matching algorithm to handle missing blocks. We test our algorithm using map data from the Cyclopath geowiki and GPS traces from Cyclopath's mobile app. Even for conservative cutoff distances, our algorithm found a significant amount of missing data per set of GPS traces. We tested the algorithm for accuracy by removing existing blocks from our map dataset. As the cutoff distance was lowered, false negatives were decreased from 34% to 16% as false positives increased from 5% to 10%. Although the algorithm degrades with increasing amounts of missing data, our results show that our extensions have the potential to improve both map matches and map data.