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.
· Communications in Transporta... · 0 citations