Cost Estimation of Metro Construction Projects Using Interpretable Machine Learning

The metro, renowned as an environmentally friendly mode of transportation due to its low energy consumption and minimal pollution, plays a crucial role in achieving sustainable urban growth. Due to the scarcity of information in the early stages of metro construction projects and the subjectivity of cost estimation (which relies heavily on the estimator's experience), it is always difficult to guarantee the accuracy of metro construction project cost estimation. Furthermore, the existing methodological models commonly used for cost estimation do not adequately consider the interpretability of the estimation results, making it difficult to promote their application in real-world scenarios. In this paper, an interpretable machine learning method is introduced into the study of cost estimation of metro construction projects, and a maximum relevance and minimum redundancy (mRMR)-light gradient boosting machine (LightGBM)-Shapley additive explanations (SHAP) interpretable assisted investment decision-making framework is proposed. The results show that the negative impact of variable multicollinearity on model prediction is avoided by quantitatively identifying the key driving variables of costing through mRMR based on the historical data of metro construction projects and macroeconomic data. LightGBM is employed to predict the cost of metro construction projects with a mean absolute percentage error of 13.00%, surpassing the accuracy of the five baseline models. The SHAP method's introduction explains the influence of key driving variables on the model prediction response at both global and local levels, which improves the decision trust of the cost estimation of metro construction projects. The study takes into account the impact of key driving variables on the model's prediction response in a real-world context and balances the needs for estimation accuracy and variable interpretability in real-world scenarios. In this study, we propose a new interpretable machine learning method that helps improve the decision trust of cost estimation for metro construction projects. We use the mRMR module to identify the key driving variables for cost. Based on the LightGBM module, we accurately fit the nonlinear relationship between the cost of metro construction projects and the key variables. The SHAP algorithm is introduced to analyze the effects of the key driving variables on the prediction of the model at both global and local levels. The combined mRMR-LightGBM-SHAP model is proven to have better prediction accuracy than the traditional prediction model under the same conditions, and the SHAP response results prove the reliability of the model prediction. The study also found that the inclusion of economic environment variables plays a positive role in improving the accuracy of cost estimation for metro construction projects. The research results can be mainly used by metro investment units to quickly confirm or review the cost of metro construction projects, and they can also help government departments pay close attention to the impact of the external economic environment on the cost of metro construction.