TravelMiner : On the benefit of path-based mobility prediction

• Mobility predictions are becoming more valuable in various applications with the rise of mobile devices. Given that existing prediction techniques are composed of two key procedures: 1) profiling past mobility trajectories as sequences of discrete atomic states (e.g., grid locations, semantic locations) and capturing them with an appropriate statistical model, 2) making a prediction on the next state using the statistical model, TravelMiner tackles the former with paths utilized as the atomic states for the first time, where the paths are defined as sub-trajectories with no branches. Comparing to available location-based predictors, TravelMiner makes a fundamental difference in that it is able to predict the sequence of paths rather than locations, which is far more detailed in the perspective of knowing the exact route to follow. TravelMiner enables this benefit by extracting disjoint paths from GPS trajectories via a similarity metric for curves, called Frechet distance and keeping the sequences of such paths in a statistical model, called probabilistic radix tree. Our extensive simulations over the GPS trajectories of 124 users reveal that TravelMiner outperforms other predictors in diverse popular performance metrics including predictability, prediction accuracy and prediction resolution.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Annan teknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Other Engineering and Technologies (hsv//eng)

Nyckelord

Clustering algorithms

Location

Semantics

Trajectories

Extensive simulations

Mobility predictions

Performance metrics

Prediction accuracy

Prediction techniques

Semantic locations

Similarity metrics

Statistical modeling

Publikations- och innehållstyp

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