There is growing pressure to manage and run urban rail transit networks as more people select this mode of transportation for their travel. It is, therefore, essential to create a precise system to predict passenger movements from origins to destinations (OD). The effectiveness of the present methods in simulating links between stations using real-time passenger flow data is limited. This paper suggests a Spatiotemporal Fusion Network for OD flow forecasting in urban rail travel. By examining previous OD data, this network creates spatiotemporal link graphs connecting stations that integrate spatiotemporal correlations of passenger flows. These graphs are incorporated into the forecasting system to forecast OD flows. Using actual passenger flow data from Shanghai and Hangzhou, we validate our MGLTN model and show almost 4% improvement in prediction accuracy (measured by MAE) over many state-of-the-art baseline models on both datasets. We also present a dwell score derived from anticipated train frequencies and passenger flows. Each station receives a rating based on this score, which represents its dwell qualities in comparison to predetermined norms. © 2025