Short-term prediction of metro passenger flow with multi-source data: A neural network model fusing spatial and temporal features

Abstract

Accurate short-term prediction of metro passenger flow can ensure the efficient operation of metro system and comfort and safety of passengers. Most existing studies use a single data source such as smart card data to predict metro passenger flow, which only consider the internal passenger flows of the metro system and fails to consider the potential passenger flows outside metro stations. However, the potential passenger flow outside metro stations actually has an impact on metro passenger flow in the short term. Using multiple sources of data including smart card data, mobile phone data and metro network data, this paper presents a neural network (NN) model for short-term prediction of metro passenger flow. In the proposed NN model, various information are taken into account by extracting spatial and temporal features inside and outside the metro system. The proposed model structure includes long short-term memory layers to extract historical pattern of the extracted features and fully connected layers to grasp potential connection and interaction among the features. The advantages of the proposed model are demonstrated by using multi-source data from Suzhou, China. The results show that the proposed NN model outperforms various baseline models in terms of accuracy and stability. It can also be found that the inclusion of mobile phone data improves the accuracy of prediction.