Addressing the urgent global concern of transitioning to sustainable energy sources involves navigating a complex landscape of opportunities and challenges. Factors such as the growing global population, dwindling fossil fuel reserves, inefficient energy consumption, and the adverse impacts of climate change, all contribute to the need for renewable and green energy solutions. The emissions linked to energy are currently at record levels, with the energy sector being accountable for nearly three-quarters of global greenhouse gas emissions. Innovation in technology, corporate processes, and regulations is vital for the energy transition to thrive on all levels. Investing funds into renewable energy technology and practices may enhance future energy systems’ resilience, equity, and sustainability. The advantages of renewable energy include minimal environmental impact, a stable supply of energy even in harsh weather conditions, and the ability to effectively reduce pollutants. Renewable energy has several benefits such as boosting economic growth, creating jobs, and improving energy security. However, there are some challenges related to renewable energy storage, which scientists are working to address. Public education is key to creating an environment that supports the expansion of renewable energy sources. Keeping up with technological advancements, industry trends, and policy updates is crucial to adjusting techniques and strategies in response to changing challenges and opportunities. To evaluate the effectiveness of innovative solutions and guide future decision-making, it is recommended to establish monitoring and evaluation systems that track the performance, impact, and outcomes of renewable energy innovations.

In the last two decades the efficient traffic-flow prediction of vehicles has been significant in curbing traffic congestions at freeways and road intersections and it is among the many advantages of applying intelligent transportation systems in road intersections. However, transportation researchers have not focused on prediction of vehicular traffic flow at road intersections using hybrid algorithms such as adaptive neuro-fuzzy inference systems optimized by genetic algorithms. In this research, we propose two models, namely the adaptive neuro-fuzzy inference system (ANFIS) and the adaptive neuro-fuzzy inference system optimized by genetic algorithm (ANFIS-GA), to model and predict vehicles at signalized road intersections using the South African public road transportation system. The traffic data used for this research were obtained via up-to-date traffic data equipment. Eight hundred fifty traffic datasets were used for the ANFIS and ANFIS-GA modelling. The traffic data comprised traffic volume (output), speed of vehicles, and time (inputs). We used 70% of the traffic data for training and 30% for testing. The ANFIS and ANFIS-GA results showed training performance of (R²) 0.9709 and 0.8979 and testing performance of (R²) 0.9790 and 0.9980. The results show that ANFIS-GA is more appropriate for modelling and prediction of traffic flow of vehicles at signalized road intersections. This research adds further to our knowledge of the application of hybrid genetic algorithms in traffic-flow prediction of vehicles at signalized road intersections.