CityMomentum: An Online Approach for Crowd Behavior Prediction at a Citywide Level

Human movements are difficult to predict, especially, when we consider rare behaviors that deviate from normal daily routines. By tracing the behavior of a person over a long period, we can model their daily routines and predict periodical behaviors, whereas rare behaviors, such as participating in the New Year's Eve countdown, can hardly be predicted readily and thus they have usually been treated as outliers of the daily routines in most existing studies. However, for scenarios such as emergency management or intelligent traffic regulation, we are more interested in rare behaviors than daily routines. Using human mobility Big Data, the rare behavior of each individual in a social crowd is no longer rare and thus it may be predicted when we analyze the crowd behavior at a citywide level. Therefore in this study, instead of predicting movement based on daily routines, we make short-term predictions based on the recent movement observations. We propose a novel model called CityMomentum as a predicting-by-clustering framework for sampling future movement using a mixture of multiple random Markov chains, each of which is a Naive Movement Predictive model trained with the movements of the subjects that belong to each cluster. We apply our approach to a big mobile phone GPS log dataset and predict the short-term future movements, especially during the Comiket 80 and New Year's Eve celebration. We evaluate our prediction by a Earth Mover Distance (EMD) based metric, and show our approach accurately predicts the crowd behavior during the rare crowd events, which makes an early crowd event warning and regulation possible in the emergent situations.