What question did this study set out to answer?

This research aims to model the competitive impact of high-speed rail on other transport modes at varying distances.

March 23, 2026Open Access

Distance-Dependent Competitive Dynamics of High-Speed Rail: A Comparative Machine Learning Approach for Predicting Mode Choice and Market Share Redistribution

Key Points

This research aims to model the competitive impact of high-speed rail on other transport modes at varying distances.
Utilized Stated Preference data from 3,139 respondents
Compared a baseline multinomial logit model with five machine learning algorithms
Performed SHAP analysis for understanding choice behaviors
Analyzed distance-dependent effects on mode choice across short, medium, and long-haul segments
Evaluated the effect of operational velocities on market dominance.
Road transport remains dominant for trips up to 300 km
A significant shift from road to rail occurs at 300-500 km
High-speed rail replaces air travel between 500-800 km
CatBoost algorithm significantly outperforms other models
An 'Air Rebound' effect arises if HSR fares exceed a price ceiling, leading to increased demand for air travel.

Abstract

• The study models the impact of HSR on other modes across various distances. • Road transport retains market dominance in short-distance (0-300 km) segments. • HSR triggers a substantial road-to-rail shift in medium-haul (300–500 km) trips. • HSR replaces air travel as the dominant mode in upper-medium (500-800 km) routes. • SHAP analysis reveals non-linear, distance-dependent choice behaviors. The introduction of high-speed rail (HSR) fundamentally alters intercity transport equilibrium, yet its competitive impact exhibits significant heterogeneity across travel distances and operational configurations. This study establishes a rigorous comparative framework to forecast mode choice behavior and market share redistribution along Thailand’s planned Bangkok-Chiang Mai corridor. Utilizing Stated Preference (SP) data from 3,139 respondents, we benchmark a baseline multinomial logit (MNL) model against five advanced machine learning (ML) algorithms. Empirical results demonstrate that ensemble tree-based models significantly outperform both MNL and deep neural networks, with CatBoost emerging as the superior classifier. Integrating SHapley Additive exPlanations (SHAP) with a comprehensive sensitivity analysis, this research uncovers distinct, distance-dependent competitive regimes and non-linear "threshold effects." In short-distance segments (0–300 km), HSR demand is primarily governed by price sensitivity. In medium-distance segments (300–500 km), a significant "road-to-rail" shift is observed, with demand saturating at an optimal operational velocity of 250 km/h. For longer distances (500–800 km), HSR achieves market dominance as a formidable air transport substitute, provided it breaches a critical 300 km/h tipping point. However, the findings reveal a fragile equilibrium: an "Air Rebound" effect occurs if HSR fares exceed a rigid price ceiling, causing demand to surge back to aviation. These results suggest that a tiered HSR service strategy—balancing high-velocity performance for long hauls with price competitiveness for regional segments—is essential to maximize commercial viability and social sustainability within a multi-modal ecosystem.

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Cite This Study

Simalorit et al. (Sun,) studied this question.

synapsesocial.com/papers/69c0df0bfddb9876e79c169f https://doi.org/https://doi.org/10.1016/j.rineng.2026.110196

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