Companion Mobility to Assist in Future Human Location Prediction

Abstract

Location prediction plays an important role in modeling human mobility. Existing studies focused on developing a prediction model which is based solely on the past mobility of only the person of interest (POI), rather than including information on the mobility of her/his companions. In fact, people frequently move in a group, and thus, using mobility data of a person's companions can enhance accuracy when predicting that person's future locations. Motivated by this, we propose a two-phase framework for predicting an individual's future locations that fully benefits from spatio-temporal contexts embedded in that person's and his/her companions' mobility. The framework first determines the POI's companions, then predicts future locations based on mobility information for both the POI and selected companions. Two companion selection methods are proposed in this work. The first method uses spatial closeness (SC) to determine the companions of the POI by measuring the similarity of the individuals' geographic distributions. The second method builds person ID embedding (PIE) vectors, and cosine similarity is used to select the POI's companions. To mitigate the curse of dimensionality, the framework also uses a stacked autoencoder in which the encoder compresses a high-dimensional input feature (e.g., location, time, and person ID) into a low-dimensional latent vector. For the second phase of the framework, a bidirectional recurrent neural network (BRNN)-based multi-output model is proposed to predict a person's future locations in the next several time slots. To train the BRNN model, weighted loss is used, which takes into account the importance of each future time slot to predict the POI's locations accurately. Experiments are conducted on two largescale Wi-Fi trace datasets, demonstrating that the proposed model can effectively predict human future locations.

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