Spatial Suitability Scoring For Rail Transit Network Extension by Developing 'Rail Transit Fuzzy Logic Model' (RFLM) in Matlab Environment

This research has focused on the fuzzy logic model for evaluating the necessity of expanding the rail transit network to achieve sustainable transport development. It is essential to distinguish input parameters that can be obtained and updated frequently in a cost-effective way. A framework that can be employed to systemize the planning process by using the 'Rail Transit Fuzzy Logic Model'. It can be characterized as a spatial suitability oriented fuzzy approach by using five inputs (slope, geology, population, land use and stream), 1 output (Suitability Score) and 108 If-Then rules to assist in combining these data sources in the MATLAB environment. This study consists of three main steps: (i) development of a fuzzy inference system (FIS), (ii) design of the graphical user interface (GUIDE toolbox), and (iii) suitability score database production for the study area. Fuzzy rule-based systems have been applied with parameters represented by fuzzy diagrams that are integrated into an aggregate form to obtain a spatial suitability score. This analysis allows us to assign suitability scores for any specific type of study area. The study area is composed of 208 rows and 216 columns that are uploaded and modelled in MATLAB R2021b as a 216-by-208 matrix. This method commonly has not been employed and processed for a rail network that covers a large geographical area spanning several regions in a city. The proposed methodology offers systematic qualitative data available for decision-makers to be used in the evaluation of rail transportation investments.