On map-matching vehicle tracking data
VLDB '05 Proceedings of the 31st international conference on Very large data bases
WhereNext: a location predictor on trajectory pattern mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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
An Interactive-Voting Based Map Matching Algorithm
MDM '10 Proceedings of the 2010 Eleventh International Conference on Mobile Data Management
Proceedings of the 13th international conference on Ubiquitous computing
Reducing Uncertainty of Low-Sampling-Rate Trajectories
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Mining large-scale, sparse GPS traces for map inference: comparison of approaches
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Urban traffic modelling and prediction using large scale taxi GPS traces
Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Pathlet learning for compressing and planning trajectories
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Locating lucrative passengers for taxicab drivers
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
| Hi-index | 0.00 |
We present a robust method for solving the map matching problem exploiting massive GPS trace data. Map matching is the problem of determining the path of a user on a map from a sequence of GPS positions of that user --- what we call a trajectory. Commonly obtained from GPS devices, such trajectory data is often sparse and noisy. As a result, the accuracy of map matching is limited due to ambiguities in the possible routes consistent with trajectory samples. Our approach is based on the observation that many regularity patterns exist among common trajectories of human beings or vehicles as they normally move around. Among all possible connected k-segments on the road network (i.e., consecutive edges along the network whose total length is approximately k units), a typical trajectory collection only utilizes a small fraction. This motivates our data-driven map matching method, which optimizes the projected paths of the input trajectories so that the number of the k-segments being used is minimized. We present a formulation that admits efficient computation via alternating optimization. Furthermore, we have created a benchmark for evaluating the performance of our algorithm and others alike. Experimental results demonstrate that the proposed approach is superior to state-of-art single trajectory map matching techniques. Moreover, we also show that the extracted popular k-segments can be used to process trajectories that are not present in the original trajectory set. This leads to a map matching algorithm that is as efficient as existing single trajectory map matching algorithms, but with much improved map matching accuracy.