Comparing Sequential and Temporal Patterns from Human Mobility Data for Next-Place Prediction
Published in Adjunct Proceedings of the 2018 Conference on User Modeling Adaptation and Personalization (UMAP 2018), 2018
In the domain of human behavior prediction, next-place prediction is an active research field. While prior work has applied sequential and temporal patterns for next-place prediction, no work has yet studied the prediction performance of combining sequential with temporal patterns compared to using them separately. In this paper, we address next-place prediction using the sequential and temporal patterns embedded in human mobility data that has been collected using the GPS sensor of smartphones. We test five next-place prediction methods, including single pattern-based methods and hybrid methods that combine temporal and sequential patterns. Instead of only examining average accuracy as in related work, we additionally evaluate the selected methods using incremental-prediction accuracy on two publicly available datasets (the MDC dataset and the StudentLife dataset). Our results suggest that (1) integrating multiple patterns is not necessarily more effective than using single patterns in average prediction accuracy, (2) most of the tested methods can outperform others for a certain time period (either for the prediction of all places or each place individually), and (3) average prediction accuracies of the top-three candidates using sequential patterns are relatively high (up to 0.77 and 0.91 in the median for both datasets). For real-time applications, we recommend applying multiple methods in parallel and choosing the prediction of the best method according to incrementalprediction accuracy. Lastly, we present an expert tool for visualizing the prediction results.
Yunlong Wang, Corinna Breitinger, Björn Sommer, Falk Schreiber, and Harald Reiterer. "Comparing Sequential and Temporal Patterns from Human Mobility Data for Next-Place Prediction." In Adjunct Proceedings of the 2018 Conference on User Modeling Adaptation and Personalization (UMAP 2018). ACM, 2018. DOI: 10.1145/3213586.3226212.