Assessing the Influence of Spatio-Temporal Context for Next Place Prediction using Different Machine Learning Approaches

Abstract

For next place prediction, machine learning methods which incorporate contextual data are frequently used. However, previous studies often do not allow deriving generalizable methodological recommendations, since they use different datasets, methods for discretizing space, scales of prediction, prediction algorithms, and context data, and therefore lack comparability. Additionally, the cold start problem for new users is an issue. In this study, we predict next places based on one trajectory dataset but with systematically varying prediction algorithms, methods for space discretization, scales of prediction (based on a novel hierarchical approach), and incorporated context data. This allows to evaluate the relative influence of these factors on the overall prediction accuracy. Moreover, in order to tackle the cold start problem prevalent in recommender and prediction systems, we test the effect of training the predictor on all users instead of each individual one. We find that the prediction accuracy shows a varying dependency on the method of space discretization and the incorporated contextual factors at different spatial scales. Moreover, our user-independent approach reaches a prediction accuracy of around 75%, and is therefore an alternative to existing user-specific models. This research provides valuable insights into the individual and combinatory effects of model parameters and algorithms on the next place prediction accuracy. The results presented in this paper can be used to determine the influence of various contextual factors and to help researchers building more accurate prediction models. It is also a starting point for future work creating a comprehensive framework to guide the building of prediction models.

From Motion Activity to Geo-Embeddings: Generating and Exploring Vector Representations of Locations, Traces and Visitors through Large-Scale Mobility Data

“…In case of a serious sparsity of trajectory data leading to many unique locations with a very low number of occurrences, which may cause high computational cost and may not achieve a good result in terms of embedding representation of places, we suggest to group together adjacent locations. Existing methods utilize cell-based partitioning, clustering, and stop point detection to transform trajectories into discrete cells, clusters and stay points [38]. A valuable option may be to fix some meaningful reference points on the territory and project the other locations to the nearest fixed point.…”

On (The Lack Of) Location Privacy in Crowdsourcing Applications

“…Inspired by previous works, we use clustering to implement inference on the regions and the points of interest for all users. [36,42,46,49,59,64,69,86]. Concretely, we use the density-based clustering algorithm (DBSCAN) [46].…”

“…Similar to [36,49,58,59,64,66,67,69,86], our POIs extraction attack is based on machine learning. Gambs and Killijian [52] also rely on POIs inference to build mobility Markov chains and de-anonymize traces.…”

LSTM-Based Deep Learning Model for Predicting Individual Mobility Traces of Short-Term Foreign Tourists

“…A final subgroup includes motion pattern mining techniques and prediction algorithms combining individual current movements with historical collective data to find frequent patterns and co-occurrences of locations. The methods comprise ensemble probabilistic algorithms [46,47], feature-based machine learning methodologies [48,49], and deep learning models [50,51] to predict users' locations over time, based on individual and collective behaviors.…”