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Background
Nepal’s road transport network reflects a deep and persistent regional imbalance, shaped by the country’s rugged terrain and socio-political history. While the southern Terai plains enjoy relatively good connectivity due to their flat landscape and proximity to economic centers, the hilly and mountainous regions—particularly in the Mid-Western and Far-Western districts—remain severely underserved. Districts such as Dolpa, Jumla, Bajura, and Jajarkot struggle with minimal road coverage, poor maintenance, and limited year-round accessibility. These disparities have profound implications, restricting access to healthcare, education, and economic opportunities for millions of people living in remote areas.
The issue is not just geographical. Nepal’s demographic and development diversity further complicates infrastructure planning. Densely populated urban districts like Kathmandu, Lalitpur, and Bhaktapur have benefited from sustained investments in road networks, driven by demand and economic activity. In contrast, sparsely populated and economically marginalized regions receive less attention, leading to a mismatch between infrastructure provision and actual need. As a result, travel times in mountainous districts can be five to ten times higher than in urban centers, exacerbating regional inequality and economic isolation.
Moreover, road infrastructure investment has often followed traditional population metrics rather than holistic indicators of socio-economic vulnerability. This approach overlooks critical factors such as the dependent age ratio, poverty levels, and accessibility to essential services, which are more pronounced in remote regions. For instance, districts with high development shortfalls and limited employment opportunities are not necessarily prioritized in road planning, despite having greater mobility needs. The consequences are visible—people in these areas are cut off during monsoons, struggle with market access, and face delays in emergency services.
Current Study
(a) Needed Road Network (b) Current Road Network
In my recent study currently under review (Jaiswal et al., 2025), we apply a machine learning-based framework to assess these disparities across Nepal’s 77 districts. The model integrates demographic, economic, and spatial indicators to develop a Transport Network Need Index (TNNI) and an Index of Disparity between Needs and Provision (IDNP). Our findings highlight districts like Jajarkot, Jumla, and Bajura as critically underserved, with a clear misalignment between infrastructure needs and existing road density. Once published, this research will provide a data-driven roadmap for equitable infrastructure investment in Nepal—ensuring that underserved regions are no longer left behind.
References
Central Bureau of Statistics. (2011). Population Atlas of Nepal. Government of Nepal.
Jaiswal, R., Jha, M. K., & Yadav, R. (2025). Investigating Regional Disparities in Nepal’s Road Network with Machine Learning. In 3rd International Conference Transportation Infrastructure Projects: Conception to Execution (TIPCE), Roorkee, India, September 2025.
Chhetri, N. P., Jaiswal, R., & Poudel, R. (2024). Resilient roads in chal-lenging terrain: a case study of Siddhartha highway in Nepal. Discover Civil Engineering, 1(1), 1-16.
OECD (2020). Transport infrastructure trends and regional development. Transport Bridging Divides, OECD Publishing, Paris.
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