ORIGINAL RESEARCH article

Front. Built Environ.

Sec. Transportation and Transit Systems

Volume 11 - 2025 | doi: 10.3389/fbuil.2025.1601754

This article is part of the Research TopicAdvancements in Traffic Safety: Data-Driven Insights and Emerging TechnologiesView all articles

Elderly Travel Mode Choice in Thailand-Evaluating MNL and Machine Learning Models

Provisionally accepted
  • 1Suranaree University of Technology, Nakhon Ratchasima, Thailand
  • 2Rajamangala University of Technology Isan, Nakhon Ratchasima, Nakhon Ratchasima, Thailand

The final, formatted version of the article will be published soon.

This investigation analyzes the determinants of transportation mode selection among elderly populations in Thailand through a comparative approach utilizing both traditional statistical modeling and contemporary machine learning techniques. The research compares the predictive effectiveness of the Multinomial Logistics Regression (MNL) model against advanced algorithmic approaches including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost) in forecasting elderly travel behavior. The research utilizes a dataset comprising 1,000 elderly participants distributed across Thailand's four geographical regions, with data collection conducted via structured questionnaires encompassing demographic factors, journey purposes, frequency of travel, expenditure considerations, and modal preferences. Results indicate that the Random Forest algorithms achieved the highest predictive performance on the comprehensive dataset (99.83% accuracy), while CatBoost demonstrated excellent performance on test data (94%). Shapley Additive Explanations (SHAP) analysis identified transportation expenditure, travel party size, temporal considerations, and economic status as the predominant variables influencing modal selection decisions, with lower-income elderly individuals showing a greater tendency for public transportation utilization relative to their more affluent counterparts. The MNL model revealed transportation cost as the most statistically significant predictor of public transit usage (p<0.001), indicating that elderly individuals confronted with elevated travel expenses tend to substitute public transportation with private vehicle alternatives. Concurrently, machine learning methodologies demonstrated enhanced capacity to capture complex relationships between predictive factors and exhibited superior predictive accuracy compared to conventional MNL modeling. These findings offer important implications for the formulation of age-sensitive public transportation policies, particularly emphasizing cost reduction strategies and infrastructure enhancements designed to accommodate the specific mobility requirements of elderly populations.Country Methodology Data Used Key Findings Accuracy (%) Wei et al. (2024) China Random Forest Bus ridership data

Keywords: Elderly, Travel mode choice, Multinomial logit, machine learning, CatBoost, Shap, XGBoost, random forest

Received: 28 Mar 2025; Accepted: 30 May 2025.

Copyright: © 2025 Philuek, Wisutwattanasak, Watcharamaisakul, Banyong, Chantaratang, Champahom, Ratanavaraha and Jomnonkwao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Sajjakaj Jomnonkwao, Suranaree University of Technology, Nakhon Ratchasima, Thailand

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