A machine learning comparison of transportation mode changes from high-speed railway promotion in Thailand

Thailand's collaboration with China to develop High-Speed Rail (HSR) represents a crucial step in enhancing transportation infrastructure and promoting regional economic growth. While most research has focused on existing travel modes such as cars, buses, and planes, there is a notable lack of analysis on future transport choices, particularly in developing countries like Thailand. This study addresses this gap by analyzing the factors influencing High-Speed Railways adoption to inform future traveler decisions. The comparison of models for predicting High-Speed Railways usage revealed that CatBoost consistently outperformed the other models, with cross-validation confirming its superior performance across all key metrics. The Binary Logit Model (BL) demonstrated moderate effectiveness, achieving an accuracy of 0.7404 ± 0.006, sensitivity of 0.7655 ± 0.006, and a relatively lower AUC of 0.8161 ± 0.0067. Its specificity (0.7140 ± 0.0129), precision (0.7376 ± 0.0085), and F1 score (0.7513 ± 0.0055) were moderate, but it struggled with handling imbalanced data. In contrast, XGBoost delivered significantly stronger results, with an accuracy of 0.8846 ± 0.0068, sensitivity of 0.9210 ± 0.0094, and an AUC of 0.9583 ± 0.0036. XGBoost also achieved high specificity (0.8464 ± 0.0062), precision (0.8630 ± 0.0055), and an F1 score (0.8910 ± 0.0067). LightGBM also performed well, achieving an accuracy of 0.8763 ± 0.0077 and a relatively high sensitivity of 0.9358 ± 0.0071. However, it exhibited lower specificity (0.8139 ± 0.0107) and precision (0.8408 ± 0.0083), resulting in a slightly reduced F1 score of 0.8857 ± 0.0069 and an AUC of 0.9506 ± 0.0034. CatBoost demonstrated the highest overall performance, achieving an accuracy of 0.8853 ± 0.0061, sensitivity of 0.9245 ± 0.0072, specificity of 0.8441 ± 0.0068, precision of 0.8616 ± 0.0058, an F1 score of 0.8920 ± 0.0058, and an AUC of 0.9584 ± 0.0034. The decision to use High-Speed Railways in Thailand is influenced by several key factors that reflect the country's unique transportation context. These factors include travel time, access time, service frequency, cost, waiting time, household income, automobile ownership, gender, and the purpose of travel. This information is valuable for shaping policies to support High-Speed Railways adoption in Thailand. • Assessing the results of models for machine learning. • Machine learning outperforms binary logit models in decision prediction. • SHAP demonstrated the effectiveness of XGBoost, LightGBM, and CatBoost. • Findings guided policies to promote future high-speed rail use.