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Research Article | Open Access

From capacity maximization to flagship train optimization: a novel framework for brand-oriented railway timetabling

China Railway Train Working Diagram Technology Center, Beijing, China
China Academy of Railway Sciences Corporation Limited, Transportation and Economics Research Institute, Beijing, China
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Abstract

Purpose

This study investigates the impact of flagship trains on high-speed railway capacity utilization and develops a brand value-oriented optimization framework that balances service quality enhancement with operational efficiency.

Design/methodology/approach

A mathematical optimization model based on integer programming is developed, incorporating flagship train constraints into capacity optimization. Case studies compare scenarios with and without flagship train considerations using the Beijing–Shanghai High-Speed Railway data across 20 experimental groups.

Findings

Operating flagship trains with hourly departure constraints results in an average decrease of 0.9 trains and an 8.4% reduction in capacity utilization rate. When scheduling 2 flagship trains within a 2-h timeframe, capacity utilization decreases from 86.43% to 83.73%, quantifying the trade-off between brand positioning and operational capacity.

Originality/value

This research provides the first quantitative framework for brand value-oriented railway capacity optimization, establishing clear definitions for flagship trains and mathematical foundations for evaluating service quality versus efficiency trade-offs. The findings offer practical decision support for railway operators balancing competitive positioning with capacity maximization.

References

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Railway Sciences
Pages 100-116

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Cite this article:
Xu H. From capacity maximization to flagship train optimization: a novel framework for brand-oriented railway timetabling. Railway Sciences, 2026, 5(1): 100-116. https://doi.org/10.1108/RS-09-2025-0037

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Received: 08 September 2025
Revised: 28 September 2025
Accepted: 28 September 2025
Published: 01 February 2026
© Huizhang Xu. Published in Railway Sciences.

This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at Link to the terms of the CC BY 4.0 licence.