Research Paper#Urban Planning, Mobility Prediction, Machine Learning, Interpretability🔬 ResearchAnalyzed: Jan 3, 2026 20:01
AMBIT: Improving OD Flow Prediction with Interpretable Trees
Published:Dec 27, 2025 04:59
•1 min read
•ArXiv
Analysis
This paper addresses the crucial trade-off between accuracy and interpretability in origin-destination (OD) flow prediction, a vital task in urban planning. It proposes AMBIT, a framework that combines physical mobility baselines with interpretable tree models. The research is significant because it offers a way to improve prediction accuracy while providing insights into the underlying factors driving mobility patterns, which is essential for informed decision-making in urban environments. The use of SHAP analysis further enhances the interpretability of the model.
Key Takeaways
- •AMBIT is a gray-box framework that combines physical mobility baselines with interpretable tree models for OD flow prediction.
- •The framework uses gradient-boosted trees to learn residuals on top of physical baselines.
- •POI-anchored residuals are consistently competitive and robust under spatial generalization.
- •The paper provides a reproducible pipeline and spatial error analysis for urban decision-making.
Reference
“AMBIT demonstrates that physics-grounded residuals approach the accuracy of a strong tree-based predictor while retaining interpretable structure.”