Location Embedding and Deep Convolutional Neural Networks for Next Location Prediction

We focus in this work on predicting the next location of mobile users by analyzing large data sets of the history of their movements. We make use of past location sequences to train a classification model that will be used to predict future locations. Contrary to traditional mobility prediction techniques based on Markovian models, we investigate the use of modern deep learning techniques such as the use of Convolutional Neural Networks (CNNs). Inspired by the word2vec embedding technique used for the next word prediction, we present a new method called loc2vec in which each location is encoded as a vector whereby the more often two locations cooccur in the location sequences, the closer their vectors will be. Using the vector representation, we divide long mobility sequences into several sub-sequences and use them to form Mobility Subsequence Matrices on which we run CNN classification which will be used later for the prediction. We run extensive testing and experimentation on a subset of a large real mobility trace database made publicly available through the CRAWDAD project. Our results show that loc2vec embedding and CNN-based prediction provide significant improvement in the next location prediction accuracy compared to state-of-the-art methods. We also show that transfer learning on existing pretrained CNN models provides further improvement over CNN models build from scratch on mobility data. We also show that our loc2vec-CNN model enhanced with transfer learning achieves better results than other variants including our other proposal onehot-CNN and existing Markovian models.