Enhancing Traffic Intelligence in Smart Cities Using Sustainable Deep Radial Function

被引:13
作者
Ismaeel, Ayad Ghany [1 ]
Mary, Jereesha [2 ]
Chelliah, Anitha [3 ]
Logeshwaran, Jaganathan [4 ]
Mahmood, Sarmad Nozad [5 ]
Alani, Sameer [6 ]
Shather, Akram H. [7 ]
机构
[1] Al Kitab Univ, Comp Technol Engn Coll Engn Technol, Kirkuk 36001, Iraq
[2] Annai Velankanni Coll Engn, Potalkulam 629401, Kanyakumari, India
[3] Saveetha Inst Med & Tech Sci SIMATS, Saveetha Sch Engn, Dept Comp Sci & Engn, Chennai 602117, India
[4] Sri Eshwar Coll Engn, Dept Elect & Commun Engn, Coimbatore 641202, India
[5] Northern Tech Univ, Elect & Control Engn Tech Tech Engn Coll, Kirkuk 36001, Iraq
[6] Univ Anbar, Comp Ctr, Baghdad 55431, Iraq
[7] Al Kitab Univ, Dept Comp Engn Technol, Altun Kopru 36001, Kirkuk, Iraq
基金
英国科研创新办公室;
关键词
traffic intelligence; radial basis function; traffic prediction; urban mobility; deep learning; MOBILITY;
D O I
10.3390/su151914441
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:24
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