Implementation of descriptive similarity for decision making in smart cities

Abstract

The paper deals with forming the descriptive similarity based on algorithm and a computer program for the decision making support in order to select the suitable solution for implementation from portfolio of the existing experiences related to public transport from various cities, especially in Smart Cities. It helps to satisfy needs in six fields of Smart City and to form rapid decisions for the problems solving. The deeper focus of the work is to develop tools for supporting a decision-making process in which computer systems and people inevitability to participate together. People will not be able to process and analyze the required amounts of data within the required time. However, computers cannot, in principle, decide for humans what to consider as equivalent, what is appropriate and inappropriate for humans. This work focuses on those aspects, which are related to the numerical evaluation of similarity that are needed to make decisions based on analogy, higher prevision of descriptions.