Discovery of convoys in trajectory databases
Proceedings of the VLDB Endowment
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
Map matching and uncertainty: an algorithm and real-world experiments
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Swarm: mining relaxed temporal moving object clusters
Proceedings of the VLDB Endowment
MoveMine: Mining moving object data for discovery of animal movement patterns
ACM Transactions on Intelligent Systems and Technology (TIST)
The elements of probabilistic time geography
Geoinformatica
On the levy-walk nature of human mobility
IEEE/ACM Transactions on Networking (TON)
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Interpolation of motion data is a challenging problem that is often overlooked by researchers when using GPS data with low sampling rate. In this paper we define a spatio-temporal query called Historical Spatio-Temporal Query (HSTQ). This query receives a time and returns a waypoint containing spatio-temporal data of a moving object at that particular time. To respond to the query for any given time (e.g. every second), we need to interpolate missing waypoints of the trajectory. Linear Interpolation (LI) is the most commonly used method although it can be grossly inaccurate. To deal with this problem, we propose a method called Map-Based Interpolation (MBI). This method uses routing web-services to find significant points (path) between two waypoints. Instead of using road networks, which is typically used by methods such as map-matching, we send a query to a routing web service and analyse the returned data to find missing waypoints. To be able to respond to queries sent to HSTQ for any input time within a trajectory period, we use a combination of MBI and LI (MBI+LI) to interpolate the missing data. We also propose two measures for comparing performance of our interpolation method with LI. Experimental results using realistic trajectories show significant improvement in quality and accuracy of interpolated and down-sampled trajectory data using HSTQ with MBI+LI in comparison to the earlier, widely used LI method.