A Model Based Decision Support System for Smart Cities

Abstract

The escalating population growth and urbanization have led to a surge in the demand for smart cities. Nonetheless, handling and evaluating the vast data produced by Internet of Things (IoT) sensors requires significant effort. Therefore, implementing intelligent decision support systems is crucial for analyzing real-time data and optimizing city operations while tackling uncertain events. This study discusses the architectural flow diagram of a smart city decision support system that employs reinforcement learning techniques to enhance traffic management, minimize energy consumption, elevate public safety, and reduce risks in a constantly changing and unpredictable environment. This system comprises various components that work in tandem to provide customized real-time recommendations for a given situation. The capacity of the system to produce recommendations in real-time while taking into account the likelihood of various outcomes has the potential to enhance performance and facilitate more efficient decision-making in intricate settings. In general, this system will exhibit the capability to improve emergency response and public safety to a considerable extent in smart cities.