An efficiency-enhanced deep learning model for citywide crowd flows prediction

Abstract

The crowd flows prediction plays an important role in urban planning management and urban public safety. Accuracy is a challenge for predicting the flow of crowds in a region. On the one hand, crowd flow is influenced by many factors such as holidays and weather. On the other hand, sample data about crowd flows are generally high-dimensional, which not only has a negative impact on the prediction accuracy but also increases computational complexity. In this paper, an efficiency-enhanced model is constructed for predicting citywide crowd flows based on multi-source data using deep learning techniques. Specifically, a data reconstruction mechanism is built with Bernoulli restricted Boltzmann machine (BRBM), for the purpose of reducing the dimension of sample data. A collaborative prediction mechanism is introduced to improve the prediction accuracy of crowd flows, in which a spatio-temporal data oriented prediction model is constructed based on bottleneck residual network that can reduce the effectively computational complexity of model training, and an auxiliary prediction to further optimize the prediction accuracy based on the fully-connected network. The proposed method is evaluated by using two open datasets. The experimental results show that our method can significantly improve the prediction accuracy and reduce the training time of the prediction model, compared with other methods.