Name
Abstract | ||
|---|---|---|
With a large proportion of people carrying location-aware smartphones, we have an unprecedented platform from which to understand individuals and predict their future actions. This work builds upon the Context Tree data structure that summarises the historical contexts of individuals from augmented geospatial trajectories, and constructs a predictive model for their likely future contexts. The Predictive Context Tree (PCT) is constructed as a hierarchical classifier, capable of predicting both the future locations that a user will visit and the contexts that a user will be immersed within. The PCT is evaluated over real-world geospatial trajectories, and compared against existing location extraction and prediction techniques, as well as a proposed hybrid approach that uses identified land usage elements in combination with machine learning to predict future interactions. Our results demonstrate that higher predictive accuracies can be achieved using this hybrid approach over traditional extracted location datasets, and the PCT itself matches the performance of the hybrid approach at predicting future interactions, while adding utility in the form of context predictions. Such a prediction system is capable of understanding not only where a user will visit, but also their context, in terms of what they are likely to be doing. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Artificial Intelligence | Geospatial analysis,Data mining,Computer science,Tree (data structure),Artificial intelligence,Hierarchical classifier,Machine learning,Prediction system |
DocType | Volume | Citations |
Journal | abs/1610.01381 | 0 |
PageRank | References | Authors |
0.34 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alasdair Thomason | 1 | 15 | 3.58 |
| Nathan Griffiths | 2 | 115 | 15.49 |
| Victor Sanchez | 3 | 144 | 31.22 |