Optimization of road route alignment: a systematic literature review with meta analysis

This systematic literature review (SLR) integrates Geographic Information Systems (GIS), deep learning, and Multi-Criteria Decision Making (MCDM) to enhance road route optimization, crucial for global infrastructure development. This SLR aims to identify existing research trends, methodologies, research gaps and propose a generalized framework for streamlining the road route optimization process. The review addresses three key research questions: RQ-1. The application of deep learning for Land Use and Land Cover (LULC) classification, RQ-2. The use of MCDM techniques in road route alignment and RQ-3. Techniques for optimizing road route alignment. Utilizing PRISMA, we assessed 370 papers, selected 132 through full-text evaluation, and added 25 via. snowball sampling, totalling 157 records for analysis. The results reveal trends in current research, geographical distribution and the evolution of methodologies. It is found that Deep learning techniques significantly improve LULC classification accuracy, while MCDM techniques enable a holistic approach to road route alignment by incorporating diverse factors. The proposed generalized framework outlines a systematic approach encompassing problem definition, criteria selection, data preparation, deep learning-based LULC classification, MCDM and Least Cost Path analysis for road route alignment. This work uniquely identifies research trends, methodologies, and gaps in road route optimization, addressing three specific research questions (RQ-1 to RQ-3) on deep learning (LULC classification), MCDM techniques, and route alignment optimization. This work also highlights the scope for integrating emerging technologies, enhancing MCDM approaches, promoting cross-disciplinary collaboration, addressing data availability and quality, conducting case studies, emphasizing sustainability, resilience and focusing on global and regional contexts. This SLR will surely contribute to the development of efficient, sustainable and equitable road route optimization strategies for better infrastructure planning and worldwide development.