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Jenny F. Calderin

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Open access Aug 2026

GSPINN: A Graph Sequential Physics-Informed Surrogate for Trip Travel Time Prediction

This work shows that embedding physically meaningful structure into learning objectives is an effective strategy for traffic surrogate modeling, yielding models that maintain competitive predictive accuracy while substantially improving directional behavioral consistency.

Blessing Itoro Afolayan, Arka Ghosh, Santhanakrishnan Narayanan et al. · 0 citations

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