Lifting the Predictability of Human Mobility on Activity Trajectories

Abstract

Mobility prediction has recently attracted plenty of attention since it plays an important part in many applications ranging from urban planning and traffic forecasting to location-based services, including mobile recommendation and mobile advertisement. However, there is little study on exploiting the activity information, being often associated with the trajectories on which prediction is based, for assisting location prediction. To this end, in this paper, we propose a Time-stamped Activity INference Enhanced Predictor (TAINEP) for forecasting next location on activity trajectories. In TAINEP, we propose to leverage topic models for dimension reduction so as to capture co-occurrences of different time-stamped activities. It is then extended to incorporate temporal dependence between topics of consecutive time-stamped activities to infer the activity which may be conducted at the next location and the time when it will happen. Based on the inferred time-stamped activities, a probabilistic mixture model is further put forward to integrate them with commonly-used Markov predictors for forecasting the next locations. We finally evaluate the proposed model on two real-world datasets. The results show that the proposed method outperforms the competing predictors without inferring time-stamped activities. In other words, it lifts the predictability of human mobility.