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research-article

Data information processing of traffic digital twins in smart cities using edge intelligent federation learning

Authors:

Zhihan LvAuthors Info & Claims

Volume 60, Issue 2

https://doi.org/10.1016/j.ipm.2022.103171

Published: 01 March 2023 Publication History

Abstract

The present work analyzes the application of deep learning in the context of digital twins (DTs) to promote the development of smart cities. According to the theoretical basis of DTs and the smart city construction, the five-dimensional DTs model is discussed to propose the conceptual framework of the DTs city. Then, edge computing technology is introduced to build an intelligent traffic perception system based on edge computing combined with DTs. Moreover, to improve the traffic scene recognition accuracy, the Single Shot MultiBox Detector (SSD) algorithm is optimized by the residual network, form the SSD-ResNet50 algorithm, and the DarkNet-53 is also improved. Finally, experiments are conducted to verify the effects of the improved algorithms and the data enhancement method. The experimental results indicate that the SSD-ResNet50 and the improved DarkNet-53 algorithm show fast training speed, high recognition accuracy, and favorable training effect. Compared with the original algorithms, the recognition time of the SSD-ResNet50 algorithm and the improved DarkNet-53 algorithm is reduced by 6.37ms and 4.25ms, respectively. The data enhancement method used in the present work is not only suitable for the algorithms reported here, but also has a good influence on other deep learning algorithms. Moreover, SSD-ResNet50 and improved DarkNet-53 algorithms have significant applicable advantages in the research of traffic sign target recognition. The rigorous research with appropriate methods and comprehensive results can offer effective reference for subsequent research on DTs cities.

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Index Terms

Data information processing of traffic digital twins in smart cities using edge intelligent federation learning
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning
    1. Learning paradigms
    2. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Information Processing and Management: an International Journal

Information Processing and Management: an International Journal Volume 60, Issue 2

Mar 2023

1443 pages

ISSN:0306-4573

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Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 March 2023

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