Building construction norms for urban railways in Vietnam: Theoretical and practical foundations

  • Bùi Thị Ngọc Lan
Keywords: Urban railways, Cost estimation standards, Experience, Vietnam, Solutions

Abstract

At present, the rapid pace of urbanization in Vietnam is generating major challenges for urban transport systems, including increasing congestion, pollution, and traffic accidents. Consequently, the development of urban railways is considered by the Government to be an urgent solution, with a plan for Hanoi and Ho Chi Minh City to establish a 580-km network by 2035, meeting 30–35% of public passenger transport demand. However, Vietnam currently lacks a dedicated cost-norm system for urban railways. As a result, project developers must rely on foreign cost norms or those from unrelated sectors, which makes cost management difficult. Specifically, the Cat Linh–Ha Dong line applied Chinese cost norms, Metro Line No. 1 in Ho Chi Minh City used investment benchmarks from the Asian region, while Metro Line No. 2 had to develop its own project-specific norms. International experience shows that developed countries such as Japan and China have established dedicated cost-norm systems for urban railways, whereas Vietnam only has general norms applicable across sectors and several specialized norms, but none specifically for urban rail systems. Based on the research findings, the paper highlights the critical role of developing a comprehensive cost-norm framework to ensure effective, transparent, and technically appropriate cost management for the complex nature of urban railway projects. From this, the paper proposes four key solutions: (1) Establishing a set of specialized cost norms for urban railways tailored to Vietnam’s conditions; (2) Selectively adopting international experience and adapting it to domestic practice; (3) Institutionalizing a dedicated legal framework for cost norms in urban infrastructure; and (4) Gradually developing a cost-norm model that incorporates digital technologies such as BIM, AI, and Big Data.

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Published
2026-02-28