A Multi-Objective Optimization of Clustered Train Delay Propagation Model

This research investigates train delay propagation, focusing on identifying influencing factors, optimizing parameters to minimize delays, and proposing cost-effective mitigation measures using the southern intercity railway route of Thailand as a case study.

The study employs cluster analysis and multiple linear regression to formulate delay propagation models, followed by multi-objective optimization to achieve optimal variable sets. In addition, by analyzing passenger patterns across different train types, the representative models are developed to inform policy adjustments and optimize service delivery. The research focuses on aligning commercial viability with passenger needs and preferences.

Findings from this study will provide valuable insights for future planning and potential application to other rail routes. Key findings suggest that for double-track systems, effective policies include increasing train acceleration rates, adjusting the number of stops, and enhancing junction systems. For single-track systems, similar policies apply, with additional emphasis on converting to double tracks to minimize delays in train shunting.

Implementing these measures is expected to mitigate cumulative delays and enhance rail service efficiency.