Figure - uploaded by Shima Talebian
Content may be subject to copyright.
Source publication
Artificial intelligence is transforming the way cities operate by increasing efficiency and sustainability. Smart cities use artificial intelligence (AI) to optimize traffic flow, reduce energy usage, and improve public services. AI-powered systems process massive volumes of data in real time to improve urban planning and resource allocation. Howev...
Similar publications
![Figure 1: Turning Movement along circular roundabout Image from [14]](profile/Deo-Kabanga/publication/391046959/figure/fig1/AS:11431281392964274@1745403997510/Turning-Movement-along-circular-roundabout-Image-from-14_Q320.jpg)
The Effective management of roundabout is very important as a part of the current traffic infrastructures in developing countries. A comprehensive understanding of its dynamics especially at unsignalized roundabouts is imperative. In this paper, we study and present a Microscopic Simulation of traffic in an unsignalised roundabout using a Microscop...
Citations
... Although this study offers important insights into the environmental effects of various technologies, it does not directly focus on the need for essential minerals for energy transition and their implications on the energy nexus. In [34], a multi-objective mixed-integer linear programming model has been introduced for an energy system that integrates the functions of vehicle-to-home and home-to-grid. To optimize energy costs, discomfort index. ...
This research presents the energy management in an energy system, emphasizing it as a challenge centered on three main objective functions. The primary objective is to greatly lower emissions while reducing the operational costs of energy production. This initiative seeks to improve the overall user experience by applying strategies that not only fulfill but also surpass customer expectations. The third objective is to align real electricity consumption with its ideal levels for the next day, aiming to create more consistent demand patterns through a demand flexibility approach. This method emphasizes the efficient management of electrical demand by utilizing deferrable loads, which can be adjusted based on availability and needs. This method is crucial for recognizing non-dominated Pareto solutions among different objectives, facilitating a thorough assessment of trade-off solutions. The proposed methodology’s effectiveness and reliability are demonstrated via two case studies, highlighting its practical use and the potential advantages it provides in real-world situations.
