Disseminate Reduce Flexible Fuzzy linear regression model to the analysis of an IoT-based Intelligent Transportation System

Abstract

Abstract FLM with TFN coefficients that are symmetrically arranged have traditionally been used to develop fuzzy regression analysis. In this study, The FLM is generalized to the case when the dispersion of a fuzzy non-symmetric number is minimized. Here, we employ non-symmetric trapezoidal and TFN coefficients in our fuzzy linear models. Fuzzy number coefficients can be used to discover the closest approximations of non-symmetric fuzzy numbers, we suggest a fuzzy regression method leveraging existing techniques. On the basis of the suggested strategy and existing FLR-model, we finally resolve the numerical examples and also give an application of FLRM. A "smart city" is one that makes use of both traditional city services and digital ones. The system's ability to deliver services depends on the compatibility of its information technology and physical infrastructures. Some have dubbed this breakthrough "the future of innovation" since it targets all six pillars of sustainability: economic growth, social equity, effective governance, mobility options, environmental protection, and improved quality of life. Consideration should be given to mobility, one of the six pillars of smart cities. Interstate travel is impacted by interprovincial trade in road freight. Therefore, accurate provincial estimates of road freight transportation are crucial for enhancing rural traffic operations generally. Researchers build and test models with information from all 30 of Iran's regions in 2008. In modelling, POP is the primary independent variable. FLRM and POP are used to calibrate FLRA, and the resulting error values are used as a measure of the model's ability to match the data.