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

Abstract

Accurate and computationally efficient traffic prediction remains a fundamental challenge for transportation systems, as microscopic simulators are often too expensive for large-scale applications. This paper addresses this limitation by proposing a physics-consistent surrogate modeling framework, the Graph Sequential Physics-Informed Neural Network (GSPINN). The approach integrates graph-based spatial representation with sequence-aware path aggregation to model trip travel time. It introduces a physics-informed learning formulation that encourages monotonic relationships between travel time and key traffic variables through input-output gradient constraints. To assess robustness across varying traffic conditions, the framework is applied to four heterogeneous road networks, each characterized by distinct topology, demand patterns, and control regimes. The results show consistent predictive performance and stable behavioral properties across all settings. Complementary SHAP-based interpretability further indicates that the model captures network-specific feature dependencies in each case, providing evidence that it adapts to local traffic dynamics rather than overfitting to a single environment. In addition to accuracy and reliability, the proposed surrogate provides substantial computational advantages at inference time, achieving speed-ups of 3x to 55x. This work therefore 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.