arXiv:2606.00572v1 Announce Type: new 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
The increasing availability of public transit data and advancements in AI, specifically graph transformers, enable more sophisticated urban mobility prediction models.
Improved transit prediction algorithms provide critical insights for urban planners and operators, directly impacting city efficiency, resource allocation, and carbon footprint.
The ability to model trip-level transit with high accuracy changes how public transportation systems can be optimized, shifting from reactive to proactive management.
- · Urban planning departments
- · Public transit authorities
- · AI/ML urban tech companies
- · Smart city initiatives
- · Legacy transit planning methods
- · Inefficient urban logistics operators
Public transit systems can optimize routes and schedules in near real-time, reducing delays and improving passenger experience.
More efficient public transit could reduce private vehicle reliance, decreasing urban congestion and environmental pollution.
The widespread adoption of such predictive models could inform fundamental changes in urban design and development, prioritizing transit-oriented growth.
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Read at arXiv cs.LG