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A Comparison of Machine Learning Models in Predicting Competition Between High-Speed Rail (HSR) and Air Transport: The Tehran- Mashhad Case Study | ||
| Civil Engineering and Applied Solutions | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 03 خرداد 1405 | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22080/ceas.2026.31482.1086 | ||
| نویسندگان | ||
| Mohammad Feli1؛ Ali Naderan* 1؛ Mahmoud Saffarzadeh2 | ||
| 1Department of Civil Engineering, SR.C., Islamic Azad University, Tehran, Iran | ||
| 2Department of Civil and Environment Engineering, Tarbiat Modares University, Tehran, Iran | ||
| تاریخ دریافت: 06 فروردین 1405، تاریخ بازنگری: 03 اردیبهشت 1405، تاریخ پذیرش: 28 اردیبهشت 1405 | ||
| چکیده | ||
| Mode choice modeling represents a fundamental domain within transportation engineering and urban-regional planning. The competition between high-speed rail (HSR) and air travel holds particular significance for demand forecasting, revenue optimization, environmental policy, and infrastructure development. Traditional discrete choice models have long served as the cornerstone of mode choice analysis. These models offer interpretability, compatibility with stated and revealed preference data, and the capacity to compute policy-relevant elasticities. However, they suffer from critical limitations, such as the independence of irrelevant alternatives (IIA) assumption and inability to accommodate with large, noisy datasets. Conversely, Machine Learning (ML) methodologies have gained prominence for their capacity to handle complex, nonlinear, and high-dimensional data. By applying Artificial Neural Network (ANN), Random Forest (RF), Decision Tree (DT) and K-Nearest Neighbor (KNN), this study addresses two critical research gaps: (1) the scarcity of ML applications in developing countries with limited HSR infrastructure, and (2) the limited incorporation of psychological factors alongside socio-economic variables. Using stated preference data from 100 Iranian respondents across 18 travel scenarios, this research develops ML-based models, examining variables such as travel time, cost, income, service frequency, previous travel experiences, and psychological factors including fear of flying. The findings reveal that ANN emerged as the top performer with an overall accuracy of 84.67%. The RF model followed with 82.44% accuracy, showing robust predictive capability with relatively balanced class-wise performance, though slightly favoring the majority class. Also, class-specific analysis across all models consistently demonstrated higher precision for airplane predictions. | ||
| کلیدواژهها | ||
| High-Speed Rail؛ Mode Choice؛ Machine Learning؛ Classification Report | ||
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آمار تعداد مشاهده مقاله: 13 |
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