A novel recurrent convolutional network based on grid correlation modeling for crowd flow prediction

Abstract

Due to the increasing implementation of smart cities, crowd flow prediction has become a crucial aspect in various fields such as transportation management and public risk assessment. However, accurately predicting crowd flow can be influenced by various complex factors, including local and long-range spatial dependencies, as well as long-term temporal dependencies. Many works have attempted to model long-range spatial dependence relying on stacked convolutional layers, failing to directly consider it from a global perspective. In this article, we propose a novel recurrent convolutional network based on grid correlation, named RCNGC. In this model, the correlation between different grids based on crowd flow pattern is identified first. Then, a deep network following an encoding-prediction framework is proposed. Specifically, a novel GC-ConvLSTM module based on the grid correlation is utilized to capture long-range dependence in both temporal and spatial dimensions in encoding phase while ConvLSTM is leveraged in prediction phase. We conduct extensive experiments on a dataset in Futian, China. The results demonstrate that the proposed RCNGC is superior to baseline methods in multi-step prediction, indicating that identifying the correlation between grids preliminarily can improve the precision of prediction.