Machine Learning for Sequential Data: A Review
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
International Journal of Robotics Research
Comparisons of sequence labeling algorithms and extensions
Proceedings of the 24th international conference on Machine learning
Conditional random fields for activity recognition
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Mining Frequent Trajectories of Moving Objects for Location Prediction
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
A Hybrid Prediction Model for Moving Objects
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
WhereNext: a location predictor on trajectory pattern mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Cluster-Based Mobile Sequential Patterns in Location-Based Service Environments
MDM '09 Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware
Posterior Regularization for Structured Latent Variable Models
The Journal of Machine Learning Research
Pedestrian-movement prediction based on mixed Markov-chain model
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Semantic trajectory mining for location prediction
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Computing with Spatial Trajectories
Computing with Spatial Trajectories
Next place prediction using mobility Markov chains
Proceedings of the First Workshop on Measurement, Privacy, and Mobility
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The analysis of human location histories is currently getting an increasing attention, due to the widespread usage of geopositioning technologies such as the GPS, and also of online location-based services that allow users to share this information. Tasks such as the prediction of human movement can be addressed through the usage of these data, in turn offering support for more advanced applications, such as adaptive mobile services with proactive context-based functions. This paper addresses the problem of predicting human mobility on the basis of Hidden Markov Models (HMMs), an approach that allows us to account with location characteristics as unobservable parameters, and also to account with the effects of each individual's previous actions. We report on a series of experiments with both regular and second-order HMMs. The experiments were made with a real-world location history dataset from the LifeMap project, and the results show that a high prediction accuracy, relative to the dificulty of the task, can be achieved when considering relatively small regions.