  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.
