{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T01:46:30Z","timestamp":1772761590201,"version":"3.50.1"},"reference-count":90,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T00:00:00Z","timestamp":1749513600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004352","name":"Suranaree University of Technology (SUT), Thailand Science Research and Innovation, and National Science, Research and Innovation Fund (NSRF)","doi-asserted-by":"publisher","award":["195602"],"award-info":[{"award-number":["195602"]}],"id":[{"id":"10.13039\/501100004352","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>This study examines travel mode choice behavior within the context of Thailand\u2019s emerging high-speed rail (HSR) development. It conducts a comparative assessment of predictive capabilities between the conventional Multinomial Logit (MNL) framework and advanced data-driven methodologies, including gradient boosting algorithms (Extreme Gradient Boosting, Light Gradient Boosting Machine, Categorical Boosting) and neural network architectures (Deep Neural Network, Convolutional Neural Network). The analysis leverages stated preference (SP) data and employs Bayesian optimization in conjunction with a stratified 10-fold cross-validation scheme to ensure model robustness. CatBoost emerges as the top-performing model (area under the curve = 0.9113; accuracy = 0.7557), highlighting travel cost, service frequency, and waiting time as the most influential determinants. These findings underscore the effectiveness of machine learning approaches in capturing complex behavioral patterns, providing empirical evidence to guide high-speed rail policy development in low- and middle-income countries. Practical implications include optimizing fare structures, enhancing service quality, and improving station accessibility to support sustainable adoption.<\/jats:p>","DOI":"10.3390\/bdcc9060155","type":"journal-article","created":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T12:53:16Z","timestamp":1749559996000},"page":"155","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Machine Learning-Based Analysis of Travel Mode Preferences: Neural and Boosting Model Comparison Using Stated Preference Data from Thailand\u2019s Emerging High-Speed Rail Network"],"prefix":"10.3390","volume":"9","author":[{"given":"Chinnakrit","family":"Banyong","sequence":"first","affiliation":[{"name":"Program of Industrial and Logistics Management Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand"}]},{"given":"Natthaporn","family":"Hantanong","sequence":"additional","affiliation":[{"name":"School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, 111 University Avenue, Suranaree Sub-District, Muang District, Nakhon Ratchasima 30000, Thailand"}]},{"given":"Supanida","family":"Nanthawong","sequence":"additional","affiliation":[{"name":"School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, 111 University Avenue, Suranaree Sub-District, Muang District, Nakhon Ratchasima 30000, Thailand"}]},{"given":"Chamroeun","family":"Se","sequence":"additional","affiliation":[{"name":"Institute of Research and Development, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8555-551X","authenticated-orcid":false,"given":"Panuwat","family":"Wisutwattanasak","sequence":"additional","affiliation":[{"name":"Institute of Research and Development, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6258-496X","authenticated-orcid":false,"given":"Thanapong","family":"Champahom","sequence":"additional","affiliation":[{"name":"Department of Management, Faculty of Business Administration, Rajamangala University of Technology Isan, Nakhon Ratchasima 30000, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4620-5058","authenticated-orcid":false,"given":"Vatanavongs","family":"Ratanavaraha","sequence":"additional","affiliation":[{"name":"School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, 111 University Avenue, Suranaree Sub-District, Muang District, Nakhon Ratchasima 30000, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9369-2741","authenticated-orcid":false,"given":"Sajjakaj","family":"Jomnonkwao","sequence":"additional","affiliation":[{"name":"School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, 111 University Avenue, Suranaree Sub-District, Muang District, Nakhon Ratchasima 30000, Thailand"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102621","DOI":"10.1016\/j.jtrangeo.2019.102621","article-title":"Effectiveness of high-speed railway on regional economic growth for less developed areas","volume":"82","author":"Liang","year":"2020","journal-title":"J. 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