Multicriteria decision-making for optimized road network planning in challenging landscapes: a Saudi Arabian case study
Hussain A.M. Alyami, Saeed Alqadhi, Javed MallickPurpose
This study aims to develop a comprehensive GIS-based Fuzzy AHP framework for optimized road network planning in challenging landscapes, focusing on southwestern Saudi Arabia. The study assesses multiple geophysical, environmental, and socio-economic factors to ensure sustainable and resilient road infrastructure development.
Design/methodology/approach
The research integrates multi-criteria decision-making (MCDM) techniques, remote sensing, and Least Cost Path Analysis (LCPA) to identify the most suitable road corridors. It employs Fuzzy Analytic Hierarchy Process (Fuzzy AHP) and weighted overlay analysis within a GIS environment to evaluate 14 thematic layers, including slope, elevation, drainage density, soil texture, rock composition, road density, land use/land cover (LULC), lineament density, aspect, geology, NDVI, tourist attractions, rainfall, land ownership value, and visibility. Spatial modeling and Least Cost Path Analysis (LCPA) were utilized to determine the most cost-effective and environmentally sustainable road routes. Field surveys, expert opinions, and geospatial data validation further enhanced the study's accuracy.
Findings
The GIS-based Fuzzy AHP model effectively categorized the study area into suitability zones, identifying 5.42 km2 as highly suitable and 26.86 km2 as moderately suitable for road corridor development. The results highlight the significance of slope and elevation as key determinants for safe road construction. Least Cost Path Analysis (LCPA) demonstrated its effectiveness in selecting optimal routes that minimize construction costs and environmental impacts.
Originality/value
This study provides a structured decision-support tool for policymakers and urban planners, offering a replicable GIS-based Fuzzy AHP methodology for road corridor planning in complex terrains. The findings align with global best practices, demonstrating the broader applicability of the approach. Future research should incorporate higher-resolution datasets, explore machine learning-based weight optimization, and enhance stakeholder participation for more inclusive transportation planning.