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Next-location prediction, consisting of forecasting a user's location giventheir historical trajectories, has important implications in several fields,such as urban planning, geo-marketing, and disease spreading. Severalpredictors have been proposed in the last few years to address it, includinglast-generation ones based on deep learning. This paper tests thegeneralization capability of these predictors on public mobility datasets,stratifying the datasets by whether the trajectories in the test set alsoappear fully or partially in the training set. We consistently discover asevere problem of trajectory overlapping in all analyzed datasets, highlightingthat predictors memorize trajectories while having limited generalizationcapacities. We thus propose a methodology to rerank the outputs of thenext-location predictors based on spatial mobility patterns. With thesetechniques, we significantly improve the predictors' generalization capability,with a relative improvement on the accuracy up to 96.15% on the trajectoriesthat cannot be memorized (i.e., low overlap with the training set).

Trajectory Test-Train Overlap in Next-Location Prediction Datasets