A Variational Graph Convolution Network with Normalizing Flows for Passenger Flow Prediction

Passenger flow prediction plays a crucial role in intelligent transportation systems, aiming to facilitate affordable and eco-friendly public transportation. In this context, we introduce a comprehensive framework known as the Variational Graph Convolution Network with Normalizing Flows for Passenger Flow Prediction (VGConvNF). By combining variational graph convolution with the acquisition of a probabilistic latent variable that encodes changes in passenger flows, this innovative approach enables the modeling of dynamic passenger flows and bus stop graph structures. Furthermore, we use the power of normalizing flows to extend the range of possible distributions in order to enhance the posterior approximation. A real-world dataset of passenger flows was used to evaluate the effectiveness of our proposed method. Results demonstrate the effectiveness of our approach in extending graph neural networks with probabilistic node embeddings and enriching the core graph neural networks. This framework significantly improves the accuracy and reliability of passenger flow prediction, thus contributing to the development of efficient and sustainable transportation systems.