Enhancing Traffic Intelligence in Smart Cities Using Sustainable Deep Radial Function

Ayad Ghany Ismaeel, Jereesha Mary, Anitha Chelliah, Jaganathan Logeshwaran, Sarmad Nozad Mahmood, Sameer Alani () and Akram H. Shather
Additional contact information
Ayad Ghany Ismaeel: Computer Technology Engineering College of Engineering Technology, Al-Kitab University, Kirkuk 36001, Iraq
Jereesha Mary: Annai Velankanni College of Engineering, Potalkulam, Kanyakumari 629401, India
Anitha Chelliah: Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai 602117, India
Jaganathan Logeshwaran: Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, Coimbatore 641202, India
Sarmad Nozad Mahmood: Electronic and Control Engineering Techniques Technical Engineering College, Northern Technical University, Kirkuk 36001, Iraq
Sameer Alani: Computer Center, University of Anbar, Baghdad 55431, Iraq
Akram H. Shather: Department of Computer Engineering Technology, Al-Kitab University, Altun Kopru, Kirkuk 36001, Iraq

Sustainability, 2023, vol. 15, issue 19, 1-24

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.

Keywords: traffic intelligence; radial basis function; traffic prediction; urban mobility; deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/19/14441/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/19/14441/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:19:p:14441-:d:1252819

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().