Constructing popular routes from uncertain trajectories
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Calibrating trajectory data for similarity-based analysis
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Inferring distant-time location in low-sampling-rate trajectories
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Large-scale joint map matching of GPS traces
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
An effectiveness study on trajectory similarity measures
ADC '13 Proceedings of the Twenty-Fourth Australasian Database Conference - Volume 137
Data centric research at the University of Queensland
ACM SIGMOD Record
A framework of traveling companion discovery on trajectory data streams
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
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The increasing availability of GPS-embedded mobile devices has given rise to a new spectrum of location-based services, which have accumulated a huge collection of location trajectories. In practice, a large portion of these trajectories are of low-sampling-rate. For instance, the time interval between consecutive GPS points of some trajectories can be several minutes or even hours. With such a low sampling rate, most details of their movement are lost, which makes them difficult to process effectively. In this work, we investigate how to reduce the uncertainty in such kind of trajectories. Specifically, given a low-sampling-rate trajectory, we aim to infer its possible routes. The methodology adopted in our work is to take full advantage of the rich information extracted from the historical trajectories. We propose a systematic solution, History based Route Inference System (HRIS), which covers a series of novel algorithms that can derive the travel pattern from historical data and incorporate it into the route inference process. To validate the effectiveness of the system, we apply our solution to the map-matching problem which is an important application scenario of this work, and conduct extensive experiments on a real taxi trajectory dataset. The experiment results demonstrate that HRIS can achieve higher accuracy than the existing map-matching algorithms for low-sampling-rate trajectories.