Development of a Data-Driven Model Based on Machine Learning for Screening Urban Infrastructure Projects Considering Sustainability and Agility Dimensions
Mahyar Abasian
1
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Seyed Mohammadreza Vatankhah Ghamsari
2
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Seyed Mohammad Matin Ziaei
3
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Pooneh Hamrahi
4
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Keywords: Project Selection, Data-Driven Model, Machine Learning, Sustainability, Agility,
Abstract :
This study aims to advance the evaluation and screening process of urban infrastructure projects by designing a data-driven classification model based on the CatBoost machine learning algorithm. The proposed model classifies projects into three decision categories—Selected, Reserved, and Rejected—using 13 indicators derived from two integrated dimensions: sustainability (with its economic, social, and environmental sub-dimensions) and agility. The dataset comprises 380 real-world projects, each annotated with corresponding indicator values and expert-labeled final decisions. A rigorous statistical analysis was conducted to confirm data quality, balance, and absence of multicollinearity. Subsequently, the CatBoost model was trained and optimized via hyperparameter tuning and cross-validation techniques. Its classification performance was benchmarked against Support Vector Machine (SVM) and Artificial Neural Network (ANN) models. Results revealed the superiority of CatBoost, which achieved an accuracy of 91.21% and an F1-score of 90.32%, outperforming both alternative models across all key metrics including precision and recall. Confusion matrix analysis further highlighted its robustness in correctly identifying projects in each of the three categories. The study demonstrates that advanced machine learning models—particularly those optimized for mixed-type and nonlinear datasets—can significantly improve multi-criteria decision-making processes in urban project management. The model’s capability to integrate sustainability and agility perspectives offers a novel approach to address the complexities of modern infrastructure planning, especially in dynamic and data-rich environments. From a practical perspective, the proposed model supports urban policymakers, planners, and evaluators in selecting high-impact, future-ready projects. Theoretically, the research contributes to bridging the gap between strategic planning paradigms and intelligent computational tools. Future developments may explore integration with GIS data, real-time analytics, and adaptive learning features to expand applicability across urban development domains.
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