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

• URL: http://arxiv.org/abs/2606.00572v1
• Date: Sat, 30 May 2026 06:49:26 GMT
• Title: Spatiotemporal Multi-Task Graph Transformer for Trip-Level Transit Prediction
• Abstract summary: Existing approaches often rely on fixed temporal, spatial, or stop-leveltemporal modelling.<n>This study proposes a multi-temporal graph transformer that frames trip-level transit prediction as sequence-to-sequence modelling.<n>Key components include graph embeddings for multi-relational similarity, a context encoder for weather and temporal information, and a mixture-experts module that produces task-specific decoder representations.
• Score: 6.06184145292026
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. This study proposes SMT-GraphFormer, a spatiotemporal multi-task graph transformer that frames trip-level transit prediction as sequence-to-sequence modelling. Given a line's stop sequence and trip-level context, the model predicts successive boarding and alighting counts, with delay and dwell time treated as encoder-side surrogate tasks. Key components include graph embeddings for multi-relational stop similarity, a context encoder for weather and temporal information, and a multi-gate mixture-of-experts module that produces task-specific decoder representations for boarding and alighting predictions. Evaluation on public bus transit data from Trondheim, Norway, shows that SMT-GraphFormer outperforms stop-level tabular benchmarks, with ablation studies examining each component's contribution. The sequential formulation yields substantial gains on alighting prediction ($+$0.24 in $R^2$) and consistent improvements on boarding, delay, and dwell, confirming the value of explicit trip-level sequential bias and inter-target dependencies. These findings demonstrate the potential of transformer-based sequence modelling for capturing complex spatiotemporal dynamics in public transit and underscore the value of architectures tailored to transit data rather than off-the-shelf tabular models. The proposed framework provides a horizon-agnostic basis for scenario analysis in digital twin environments, supporting informed decision-making by planners and transit operators.

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