Anjar Dimara Sakti, Muhammad Asa, Agung Budi Harto, Tania Septi Anggraini, Cokro Santoso, Albertus Deliar, Riantini Virtriana, Akhmad Riqqi, Budhy Soeksmantono, Dudy Darmawan Wijaya, Can Trong Nguyen, Khairul Nizam Abdul Maulud, Maya Safira, Ketut Wikantika
Road infrastructure plays a vital role in national and regional development, particularly in Southeast Asia, where rapid economic growth is increasing pressure on transport systems. However, uneven investment, environmental stressors, and limited data-driven tools continue to hinder effective road maintenance planning. Previous studies have utilized remote sensing and statistical models for infrastructure analysis, but the integration of long-term environmental indicators with spatial prioritization methods remains limited. This study addresses this gap by developing a Road Maintenance Priority Index (RMPI) using ten parameters, including nighttime lights, population density, industrial zones, land surface temperature, precipitation, and wind speed. These variables were analyzed through machine learning regression and multi-criteria decision analysis to classify road segments into priority levels. Results show that 45.08 percent of roads fall into the low-priority category, followed by moderate (39.69 percent), high (9.06 percent), and very high (0.88 percent). Countries such as Singapore, Brunei, and Malaysia exhibited the highest RMPI scores, reflecting urgent maintenance needs, while Timor-Leste, Myanmar, and Laos scored lowest. The findings offer a transferable and scalable framework to support evidence-based infrastructure planning in economically and environmentally diverse regions. © 2025 The Author(s)
Geographic Information Sciences and Technology Research Group, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung, Indonesia; Center for Remote Sensing, Institut Teknologi Bandung, Bandung, Indonesia; Geographic Information Sciences, Faculty of Education and Social Sciences, Universitas Pendidikan Indonesia, Bandung, Indonesia; Center for Spatial Data Infrastructure, Institut Teknologi Bandung, Bandung, Indonesia; Geodetic Science, Engineering, Innovation Research Group, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung, Indonesia; Environment Centre, Charles University, Prague, Czech Republic; Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Selangor, Bangi, Malaysia; School of Architecture, Planning and Policy Development, Institut Teknologi Bandung, Bandung, Indonesia
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