WhereNext: a location predictor on trajectory pattern mining

By Anna Monreale, Fabio Pinelli, Roberto Trasarti, Fosca Giannotti. Knowledge Discovery and Data Mining, 2009

640 citations recorded by the source corpus.

Subfields: Building and Construction, Transportation, Signal Processing

View Paper: https://www.semanticscholar.org/paper/6988e79c4d9aa79481ec9a4a2a968438e8e62cc3

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Citations in the corpus

NextPlace: A Spatio-temporal Prediction Framework for Pervasive Systems (2011, methodology): “A prediction framework based on spatio-temporal patterns in collective mobility trajectories has been presented in [22]: this method attempts to predict the next location of a moving object by matching a new trajectory to a corpus of global frequent ones.”
Unveiling the complexity of human mobility by querying and mining massive trajectory data (2011, background): “10b), a structure designed to support the next-location prediction method in [27].”
LCARS: a location-content-aware recommender system (2013, background): “Several studies [17, 2] address the problem of predicting the future locations of moving objects by using a model (e.”
Semantic trajectory mining for location prediction (2011, methodology): “Among the general-pattern-based prediction methods, mobile sequential pattern mining techniques [6] [9] have been widely used for analyzing patterns in mobile user movement data sets.”
A Survey on Deep Learning for Human Mobility (2020, background): “[125] develop a trajectory pattern mining algorithm to represent movement patterns as sequences of regions frequently visited with a typical”
DeepMove: Predicting Human Mobility with Attentional Recurrent Networks (2018, methodology): “Early methods for mobility prediction are mostly pattern-based [4, 18, 25, 26, 42].”
Discovering popular routes from trajectories (2011, background): “The search of popular routes in light of past movements is highly relevant to trajectory processing/querying issues, including pattern mining [7], [2], [1], [8], trajectory clustering [9], [10], hot route discovery [11], [12], trajectory prediction [3], [4], [13], etc.”
Trajectory Data Mining (2015, background): “Finding such kind of patterns can benefit travel recommendation [154][34], life pattern understanding [116], next location prediction [71], estimating user similarity [107][54], and trajectory compression [90].”
Predicting future locations with hidden Markov models (2012, methodology): “Data mining techniques, on the other hand, explore frequent patterns and association rules, by defining a trajectory as an ordered sequence of time-stamped locations, and using sequence analysis methods such as modified versions of the Apriori algorithm [13, 14].”