IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i19p14441-d1252819.html
   My bibliography  Save this article

Enhancing Traffic Intelligence in Smart Cities Using Sustainable Deep Radial Function

Author

Listed:
  • 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)

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.

Suggested Citation

  • Ayad Ghany Ismaeel & Jereesha Mary & Anitha Chelliah & Jaganathan Logeshwaran & Sarmad Nozad Mahmood & Sameer Alani & Akram H. Shather, 2023. "Enhancing Traffic Intelligence in Smart Cities Using Sustainable Deep Radial Function," Sustainability, MDPI, vol. 15(19), pages 1-24, October.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:19:p:14441-:d:1252819
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/19/14441/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/19/14441/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. William Villegas-Ch & Xavier Palacios-Pacheco & Sergio Luján-Mora, 2019. "Application of a Smart City Model to a Traditional University Campus with a Big Data Architecture: A Sustainable Smart Campus," Sustainability, MDPI, vol. 11(10), pages 1-28, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ze Dong & Yipeng Zhou & Xiongguan Bao, 2024. "A Short-Term Vessel Traffic Flow Prediction Based on a DBO-LSTM Model," Sustainability, MDPI, vol. 16(13), pages 1-21, June.
    2. Junkai Zhang & Jun Wang & Haoyu Zang & Ning Ma & Martin Skitmore & Ziyi Qu & Greg Skulmoski & Jianli Chen, 2024. "The Application of Machine Learning and Deep Learning in Intelligent Transportation: A Scientometric Analysis and Qualitative Review of Research Trends," Sustainability, MDPI, vol. 16(14), pages 1-34, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jonek-Kowalska, Izabela & Musioł-Urbańczyk, Anna & Podgórska, Marzena & Wolny, Maciej, 2021. "Does motivation matter in evaluation of research institutions? Evidence from Polish public universities," Technology in Society, Elsevier, vol. 67(C).
    2. William Villegas-Ch. & Milton Roman-Cañizares & Santiago Sánchez-Viteri & Joselin García-Ortiz & Walter Gaibor-Naranjo, 2021. "Analysis of the State of Learning in University Students with the Use of a Hadoop Framework," Future Internet, MDPI, vol. 13(6), pages 1-25, May.
    3. Agustín Zaballos & Alan Briones & Alba Massa & Pol Centelles & Víctor Caballero, 2020. "A Smart Campus’ Digital Twin for Sustainable Comfort Monitoring," Sustainability, MDPI, vol. 12(21), pages 1-33, November.
    4. Mohammed Alnahhal†& Omar Antar & Ahmad Sakhrieh & Muataz Al Hazza, 2024. "Analyzing Energy Consumption in Universities: A Literature Review," International Journal of Energy Economics and Policy, Econjournals, vol. 14(3), pages 18-27, May.
    5. William Villegas-Ch & Xavier Palacios-Pacheco & Milton Román-Cañizares, 2020. "Integration of IoT and Blockchain to in the Processes of a University Campus," Sustainability, MDPI, vol. 12(12), pages 1-21, June.
    6. Hawon Chu & Jaeseong Kim & Seounghyeon Kim & Young-Kyoon Suh & Ryong Lee & Rae-Young Jang & Minwoo Park, 2020. "ST-Trie: A Novel Indexing Scheme for Efficiently Querying Heterogeneous, Spatiotemporal IoT Data," Sustainability, MDPI, vol. 12(22), pages 1-21, November.
    7. Badr Alsamani & Samir Chatterjee & Ali Anjomshoae & Peter Ractham, 2022. "Smart Space Design–A Framework and an IoT Prototype Implementation," Sustainability, MDPI, vol. 15(1), pages 1-27, December.
    8. William Villegas-Ch & Jhoann Molina-Enriquez & Carlos Chicaiza-Tamayo & Iván Ortiz-Garcés & Sergio Luján-Mora, 2019. "Application of a Big Data Framework for Data Monitoring on a Smart Campus," Sustainability, MDPI, vol. 11(20), pages 1-15, October.
    9. Vian Ahmed & Karam Abu Alnaaj & Sara Saboor, 2020. "An Investigation into Stakeholders’ Perception of Smart Campus Criteria: The American University of Sharjah as a Case Study," Sustainability, MDPI, vol. 12(12), pages 1-24, June.
    10. Yeimi Xiomara Holguín Rengifo & Juan Felipe Herrera Vargas & Alejandro Valencia-Arias, 2023. "Proposal for a Comprehensive Tool to Measure Smart Cities under the Triple-Helix Model: Capacities Learning, Research, and Development," Sustainability, MDPI, vol. 15(18), pages 1-21, September.
    11. De Jong, Martin & Joss, Simon & Taeihagh, Araz, 2024. "Smart cities as spatial manifestations of 21st century capitalism," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    12. Susie Ruqun WU & Gabriela Shirkey & Ilke Celik & Changliang Shao & Jiquan Chen, 2022. "A Review on the Adoption of AI, BC, and IoT in Sustainability Research," Sustainability, MDPI, vol. 14(13), pages 1-25, June.
    13. Israel Edem Agbehadji & Bankole Osita Awuzie & Alfred Beati Ngowi, 2021. "COVID-19 Pandemic Waves: 4IR Technology Utilisation in Multi-Sector Economy," Sustainability, MDPI, vol. 13(18), pages 1-20, September.
    14. Li Zhao & Zhi-ying Tang & Xin Zou, 2019. "Mapping the Knowledge Domain of Smart-City Research: A Bibliometric and Scientometric Analysis," Sustainability, MDPI, vol. 11(23), pages 1-28, November.
    15. Francisco Maciá Pérez & José Vicente Berna Martínez & Iren Lorenzo Fonseca, 2021. "Modelling and Implementing Smart Universities: An IT Conceptual Framework," Sustainability, MDPI, vol. 13(6), pages 1-26, March.
    16. William Villegas-Ch & Adrián Arias-Navarrete & Xavier Palacios-Pacheco, 2020. "Proposal of an Architecture for the Integration of a Chatbot with Artificial Intelligence in a Smart Campus for the Improvement of Learning," Sustainability, MDPI, vol. 12(4), pages 1-20, February.
    17. William Villegas-Ch & Xavier Palacios-Pacheco & Milton Román-Cañizares, 2020. "An Internet of Things Model for Improving Process Management on University Campus," Future Internet, MDPI, vol. 12(10), pages 1-16, September.
    18. William Villegas-Ch. & Santiago Criollo-C & Walter Gaibor-Naranjo & Xavier Palacios-Pacheco, 2022. "Analysis of Data from Surveys for the Identification of the Factors That Influence the Migration of Small Companies to eCommerce," Future Internet, MDPI, vol. 14(11), pages 1-22, October.
    19. Mochamad Arief Budihardjo & Bimastyaji Surya Ramadan & Soraya Annisa Putri & Indah Fajarini Sri Wahyuningrum & Fadel Iqbal Muhammad, 2021. "Towards Sustainability in Higher-Education Institutions: Analysis of Contributing Factors and Appropriate Strategies," Sustainability, MDPI, vol. 13(12), pages 1-14, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:19:p:14441-:d:1252819. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.