Geospatial framework for locating optimal LRT stations in the Colombo metropolitan area, Sri Lanka

Abstract

Developing countries like Sri Lanka face severe transportation challenges that arise due to increased demand driven by rapid urbanization and population growth. This highlights the necessity of long-term efficient, environmentally friendly, and viable transport solutions like Light Rail Transit (LRT). Thus, this study proposes an integrated Remote-Sensing and Geographic Information Systems (GIS) with Analytical Hierarchy Process (AHP)-based Multi-Criteria Decision Analysis (MCDA) approach to identify optimal LRT station locations within the Colombo Metropolitan Area (CMA). Eight important factors such as slope, land use, proximity to buildings, environmentally sensitive areas, socially sensitive locations, existing transportation nodes, main roads, population density were systematically weighted, analysed, and used to produce suitability maps. Station locations were carefully selected based on suitability values across 31 zones and validated through Google imagery to assess land feasibility and availability. The demand estimation and accessibility analysis were done within the 1 km buffer zones surrounding each proposed station. This confirms that the majority of selected stations have better ridership potential and good road accessibility. Despite certain limitations in datasets, this structure can be used to design sustainable urban transport planning and long-term mobility policies applicable to Colombo, especially with the integration of precise field measurements and high-resolution imagery.