Multimodal Transport Demand Forecasting via Federated Learning

Multi-source data enhances demand prediction performance by learning from multiple transport modes simultaneously. However, existing multimodal demand forecasting methods often require direct sharing of raw data, which can be infeasible or at least very difficult, due to privacy concerns or practical constraints posed by ownership of data from different institutions. This study proposes a Multimodal Transport Demand Forecasting model via Federated Learning (FL) to improve forecasting accuracy without the need for direct data sharing. In the model, a processing center is introduced to handle parameters of forecasting models trained by each private dataset, where the exact dataset does not have to be shared and sensitive private information cannot be identified. Specifically, a Fine-grained Graph Convolution Recurrent Network (F-GCRN) is designed to capture spatiotemporal correlations of each dataset, with stronger capabilities to handle dynamic latent dependencies among different demand patterns than existing multimodal demand forecasting models. The processing center distinguishes the importance of parameters sent by different modes based on the Attentive Federated Learning mechanism and returns the processing parameters to each institution. Each institution predicts the demand with the returned parameters. Evaluations on three real-world transport datasets demonstrate that the proposed model outperforms several baselines and state-of-the-art models. Overall, this study addresses the research gap of enhancing multimodal demand forecasting without the dependence on direct data sharing and illustrates that knowledge sharing via parameters sharing by FL can improve multimodal transport demand prediction.