An origin-destination passenger flow prediction system based on convolutional neural network and passenger source-based attention mechanism

An accurate origin-destination (OD) passenger flow prediction system is crucially important for urban metro operation and management. However, there are still lacking targeted prediction systems focusing on the passenger travel demand causing urban metro oversaturation. In this study, we first identify the passenger sources of the oversaturated sections and pinpoint the key part of passenger travel demand causing major oversaturation. Next, we take advantage of the attention mechanism and the masked loss function to incorporate the passenger source information into the prediction model and develop a passenger source attention mechanism based convolutional neural network (PSAM-CNN) model. Finally, the developed PSAM-CNN model is validated using the actual passenger travel demand data of Shenzhen Metro. Comparing with several state-of-art benchmark models, the PSAM-CNN model can predict the OD passenger flows causing urban metro oversaturation more accurately, providing more targeted and accurate OD flow information for improving the operation and management of urban metro.