Online Deep Ensemble Learning for Predicting Citywide Human Mobility

Abstract

Predicting citywide human mobility is critical to an effective management and regulation of city governance, especially during a rare event (e.g. large event such as New Year's celebration or Comiket). Classical models can effectively predict routine human mobility, but irregular mobility during a rare event (precedented or unprecedented), which is much more difficult to model, has not drawn sufficient attention. Moreover, the complexity and non-linearity of human mobility hinders a simple model from making an accurate prediction. Bearing these facts in mind, we propose a novel online gating neural network framework with two phases. In the offline training phase, we train a gated recurrent unit-based human mobility predictor for each day in our training set, while in the online predicting phase, we construct an online adaptive human mobility predictor as well as a gating neural network that switches among the pre-trained predictors and the online adaptive human predictor. Our approach was evaluated using a real-world GPS-log dataset from Tokyo and Osaka and achieved a higher prediction accuracy than baseline models.