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SHAP-based Ensemble Learning for Assessing Key Factors Influencing Intention to Use Intercity Railway Service

Year: 2025

Type: conference-abstract

Abstract: This study identifies and ranks key factors influencing the intention to use intercity railway services in Thailand by employing advanced machine learning algorithms and interpretability techniques. Six algorithms—Random Forest, Gradient Boosting, AdaBoost, XGBoost, LightGBM, and CatBoost—were used to build predictive models, with data split 85:15 for training and testing, and hyperparameter tuning for robustness. XGBoost achieved the highest accuracy (84.17%) and AUC (96%), closely followed by CatBoost. Class-wise, XGBoost had the highest F1-scores for “Neutral intention” (90.16%) and “High intention” (64.86%), while CatBoost demonstrated the highest precision for “No intention” (55.56%). SHAP (SHapley Additive exPlanations) was utilized for model interpretability, revealing that ease of trip planning and polite staff behaviour are the top determinants of high usage intention. XGBoost's superior predictive performance, coupled with SHAP's ability to provide transparent and interpretable insights, offers a powerful tool for policymakers and railway operators.

DOI: 10.24135/iconip19

Authors: Chamroeun Se, Thanapong Champahom, Sajjakaj Jomnonkwao, Vatanavongs Ratanavaraha

Institutions: Suranaree University of Technology, Rajamangala University of Technology Isan

Language: English

Topic: Transportation Planning and Optimization

Subfield: Transportation

Field: Social Sciences

Domain: Social Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

OpenAlex ID: W4408517468

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