Enhancing Traffic Intelligence in Smart Cities Using Sustainable Deep Radial Function

Abstract

Smart cities have revolutionized urban living by incorporating sophisticated technologies to optimize various aspects of urban infrastructure, such as transportation systems. Effective traffic management is a crucial component of smart cities, as it has a direct impact on the quality of life of residents and tourists. Utilizing deep radial basis function (RBF) networks, this paper describes a novel strategy for enhancing traffic intelligence in smart cities. Traditional methods of traffic analysis frequently rely on simplistic models that are incapable of capturing the intricate patterns and dynamics of urban traffic systems. Deep learning techniques, such as deep RBF networks, have the potential to extract valuable insights from traffic data and enable more precise predictions and decisions. In this paper, we propose an RBF-based method for enhancing smart city traffic intelligence. Deep RBF networks combine the adaptability and generalization capabilities of deep learning with the discriminative capability of radial basis functions. The proposed method can effectively learn intricate relationships and nonlinear patterns in traffic data by leveraging the hierarchical structure of deep neural networks. The deep RBF model can learn to predict traffic conditions, identify congestion patterns, and make informed recommendations for optimizing traffic management strategies by incorporating these rich and diverse data. To evaluate the efficacy of our proposed method, extensive experiments and comparisons with real-world traffic datasets from a smart city environment were conducted. In terms of prediction accuracy and efficiency, the results demonstrate that the deep RBF-based approach outperforms conventional traffic analysis methods. Smart city traffic intelligence is enhanced by the model capacity to capture nonlinear relationships and manage large-scale data sets.

Guiding Urban Decision-Making: A Study on Recommender Systems in Smart Cities

“…Academics published their works related to this subject matter within the scientific literature and highlighted the measurable and significant impact that recommender systems had on the evolution of smart cities, improving the quality of citizens' lives, as well as ensuring a healthier and sustainable environment. As a result, articles that address various topics, such as smart mobility, e.g., traffic intelligence [7], bicycle road systems [8], sustainable energy production [9], healthcare monitoring systems [10], solutions for voltage issues [11], irrigation systems in urban parks [12], and vehicle charging stations [13], have been encountered in the scientific literature.…”