A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Aggregation and comparison of trajectories
Proceedings of the 10th ACM international symposium on Advances in geographic information systems
Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
Translation-invariant mixture models for curve clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Dynamics-aware similarity of moving objects trajectories
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
A Three-Dimensional Pedestrian-Flow Simulation for High-Rising Buildings
ACRI '08 Proceedings of the 8th international conference on Cellular Automata for Reseach and Industry
RF-Based Initialisation for Inertial Pedestrian Tracking
Pervasive '09 Proceedings of the 7th International Conference on Pervasive Computing
Evaluation of Trajectory Clustering Based on Information Criteria for Human Activity Analysis
MDM '09 Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware
Human action learning via hidden Markov model
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Next place prediction using mobility Markov chains
Proceedings of the First Workshop on Measurement, Privacy, and Mobility
Predicting future locations with hidden Markov models
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
A mixed autoregressive hidden-markov-chain model applied to people's movements
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
When and where next: individual mobility prediction
Proceedings of the First ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems
Proceedings of the First ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems
TMC-pattern: holistic trajectory extraction, modeling and mining
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
Incremental Frequent Route Based Trajectory Prediction
Proceedings of the Sixth ACM SIGSPATIAL International Workshop on Computational Transportation Science
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A method for predicting pedestrian movement on the basis of a mixed Markov-chain model (MMM) is proposed. MMM takes into account a pedestrian's personality as an unobservable parameter. It also takes into account the effects of the pedestrian's previous status. A promotional experiment in a major shopping mall demonstrated that the highest prediction accuracy of the MMM method is 74.4%. In comparison with methods based on a Markov-chain model (MM) and a hidden-Markov model (HMM) (i.e., prediction rates of about 45% and 2%, respectively), the proposed MMM-based prediction method is substantially more accurate. This pedestrian-movement prediction based on MMM using tracking data will make it possible to provide so-called "adaptive mobile services" with proactive functions.