Spatiotemporal Multi-Task Graph Transformer for Trip-Level Transit Prediction

ArXi:2606.00572v1 Announce Type: new Passenger count data from public transit systems reveals urban mobility patterns and is essential for planning, operation, and optimisation. However, non-linear spatiotemporal interdependencies across stops and lines make modelling and prediction challenging. Existing approaches often rely on fixed temporal, spatial, or stop-level formulations, limiting their ability to capture within-trip evolution and network context.